ID |
Author name |
E-mail |
Authors |
Title |
Topic |
Abstract file |
Rev. 1 |
Rev. 1 ranking |
Rev. 1 comment |
Rev. 2 |
Rev. 2 ranking |
Rev. 2 comment |
Accepted as |
PDF file |
PS file |
Corrected paper |
Paper finished |
Comment |
1 |
Hisao Kuwabara |
kuwabara@ntu.ac.jp |
1 |
Perceptual Properties of Syllables Isolated from Continuous Speech for Different Speaking Rate |
Speech - speech segmentation |
1_Hisao_Kuwabara_abstract.txt |
Hynek Hermansky |
A |
|
Genevieve Baudoin |
W |
|
Reject |
1_Hisao_Kuwabara.pdf |
|
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2 |
Shu-Chuan Tseng |
tsengsc@gate.sinica.edu.tw |
1 |
Transcribing and Annotating Mandarin Conversational Dialogues |
Speech - other |
2_Shu-Chuan_Tseng_abstract.txt |
Attila Ferencz |
W |
|
Nikola Pavesic |
W |
|
Reject |
2_Shu-Chuan_Tseng.pdf |
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3 |
Jorge Grana and Miguel A. Alonso and Manuel Vilares |
grana@udc.es |
3 |
A Common Solution for Tokenization and Part-of-Speech Tagging |
Text - parsing and part-of-speech tagging |
3_Jorge_Grańa_abstract.txt |
Karel Oliva |
S |
|
Eva Hajicova |
A |
An interesting (and useful) topic, with an interesting solution. It would be interesting to see how the solution applies not only to ambiguous structures as those quote in the paper but in a more general environment |
LP |
|
3_Jorge_Grańa.ps |
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4 |
Vlastislav Dohnal |
xdohnal@fi.muni.cz |
3 |
Approximate Searching in Large Collections of Text Data |
Text - information retrieval |
4_Vlastislav_Dohnal_abstract.txt |
Hanks Patrick |
W |
|
Jaroslava Hlavacova |
W |
I would prefer to include the definition of the D-index, which appears to be the key term of the paper, to the definition of such a common thing as metric space. Without it I couldn't understand the section 3 of the article. Also the basic school artithmetic ("... a sequential search on 50,000 sentences takes about 5 minutes, which would result in a half an hour for a 6 times larger file of 300,000 sentences.") is not necessary to put into article. The definition of "edit transformation" seems to be not complete - I miss the relationship between the strings X, Y and the sequence S (probably X=S1, Y=Sn ?) What is "distance density", "read" as a noun? |
Reject |
4_Vlastislav_Dohnal.pdf |
4_Vlastislav_Dohnal.ps |
|
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5 |
Ilya Oparin |
Ulysses@io8176.spb.edu |
3 |
Differences in the Stability of Russian and Greek Speech Signal towards Noise Effects |
Speech - other |
5_Ilya_Oparin_abstract.txt |
Josef Psutka |
W |
Interesting theme. But performed experiments are described very slightly and it is not evident if obtained results can be generalized. Experiments with Greek were executed under different conditions and can be only roughly compared with those done for Russian. |
Leon Rothkrantz |
R |
-The authors want to report about 4 problems in one paper, the work seems very preliminary -the use of statistics is not sufficient, the experimental design is very poor -the researchers choose sentences, instead of isolated words, but then there is a great mpact of context -Greek people living most of the time in petersburg or in Patras are in more then one aspect very different, it is not clear why the greek language is considered and why the reserachers don't restrick themselves to the Russian language -the paper has no literature survey and no results from others are reported |
Reject |
5_Ilya_Oparin.pdf |
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6 |
Amparo Varona |
amparo@we.lc.ehu.es |
2 |
Integrating high and low smoothed LMs in a CSR system |
Speech - automatic speech recognition |
6_Amparo_Varona_abstract.txt |
E.G. Schukat-Talamazzini |
W |
|
Pavel Skrelin |
A |
|
Reject |
6_Amparo_Varona.pdf |
6_Amparo_Varona.ps |
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7 |
Imad A. Al-Sughaiyer and Ibrahim A. Al-Kharashi |
imad@kacst.edu.sa |
2 |
Rule Parser for Arabic Stemmer |
Text - automatic morphology |
7_Imad_A._Alsughaiyer_abstract.txt |
Eduard Hovy |
|
|
Steven Krauwer |
A |
|
LP |
7_Imad_A._Alsughaiyer.pdf |
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8 |
Kristîne Levâne |
kristine@ailab.mii.lu.lv |
1 |
Latvian Corpus |
Text - text/topic summarization |
8_Kristîne_Levâne_abstract.txt |
Jaroslava Hlavacova |
R |
In the Introduction there are mentioned 30 million words, in the Conclusion 3 million. |
Karel Oliva |
R |
The paper is called "Latvian Corpus" but its contents is mainly concerned= with Latvian morphology. So maybe you should consider an appropriate cha= nge of the title. The morphological analyzer of Latvian is, in addition, obviously only in = a very initial state. Accordingly, the paper presents interesting problem= s rather than solutions to them. =20 The section on XML and statistics report on a "research started only mont= h ago" (quoted form the paper) or "some attempts". I am afraid this is sl= ightly less than I would expect for TSD conference. In addition, this is in contrast with the statement that computer-aided i= nvestigation of Latvian started already in 1990. So I guess there must be= a lot of work completed, but this is somehow not mirrorred in the paper,= unfortunately. It would be also better if the Latvian examples had more complete English= translations. As for English, correct: |
Reject |
|
8_Kristîne_Levâne.ps |
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9 |
Tomaž Šef and Maja Škrjanc and Matjaž Gams |
tomaz.sef@ijs.si |
3 |
Automatic Lexical Stress Assignment of Unknown Words for Highly Inflected Slovenian Language |
Speech - text-to-speech synthesis |
9_Tomaz_Sef_abstract.txt |
Taras Vintsiuk |
|
|
Hynek Hermansky |
A |
|
LP |
9_Tomaz_Sef.pdf |
9_Tomaz_Sef.ps |
|
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10 |
Jesús Vilares |
alonso@udc.es |
5 |
Practical NLP-Based Text Indexing |
Text - information retrieval |
10_Jesús_Vilares_abstract.txt |
Vladimir Petkevic |
R |
The complex task you tackle needs other methods. For instance: HMM tagger is totally inadequate for Spanish (as for many other languages!). You should develop a rule-based tagger reflecting the system of Spanish and you need a big corpus. Without these two prerequisites you can hardly achieve good results. No substantial results are presented. You can hardly claim that your architecture is generally good enough so as to be applicable to other languages - this sounds very courageously (see the last sentence in the 1st par., page 7). You'd improve your English! There seem to be no new ideas with respect to existing methods in NLP. |
Yorick Wilks |
|
|
Reject |
|
10_Jesús_Vilares.ps |
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11 |
András Kocsor and Kornél Kovács |
kocsor@inf.u-szeged.hu |
2 |
Kernel Springy Discriminant Analysis and its Application to a Phonological Awareness Teaching System |
Speech - other |
11_Andras_Kocsor_abstract.txt |
Genevieve Baudoin |
A |
|
Attila Ferencz |
A |
|
SP |
11_Andras_Kocsor.pdf |
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12 |
Lukasz Debowski |
vk@ufal.mff.cuni.cz |
3 |
Testing the Limits - Adding a New Language to an MT System |
Text - machine translation |
12_Lukasz_Debowski_abstract.txt |
Dr. Alexander Gelboukh Kahn |
W |
Paper: Testing the Limits by Lukasz Debowski et al. Remove page number from 1st page. |
Eneko Agirre |
A |
|
Reject |
12_Lukasz_Debowski.pdf |
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13 |
Kornel Kovacs |
kkornel@inf.u-szeged.hu |
3 |
Hungarian Speech Synthesis Using a Phase Exact HNM Approach |
Speech - speech modeling |
13_Kornel_Kovacs_abstract.txt |
Nikola Pavesic |
W |
|
Josef Psutka |
A |
Nice paper with new ideas. Maybe it could be extended and presented as oral. |
Reject |
13_Kornel_Kovacs.pdf |
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14 |
Pavel Květoň and Karel Oliva |
kareloliva@hotmail.com |
2 |
Achieving an Almost Correct PoS-Tagged Corpus |
Text - parsing and part-of-speech tagging |
14_Pavel_Kveton_abstract.txt |
Eva Hajicova |
A |
The core idea of this paper is interesting and certainly valuable. However, there are two places in the paper the authors should reconsider: (a) pages 1 through 2 incl. are rather 'talkative' (Sections 0 through 3) and in some places repeat trivial considerations); they may be shortende, made more factual and thus save place for a more explicit (and exemplified) exposition in the following sections. (b) in Section 4the results should be formulated more carefully and explicitly, since it is only the collocation "within the test sections" that indicates that the authors are not so naive as to use the same data for training and testing! the present wording would make the whole experiment dubious. |
Hanks Patrick |
A |
|
LP |
|
14_Pavel_Kveton.ps |
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15 |
Rei Oguro and Hiromi Sekiya and Yuhei Morooka and Kazuyuki Takagi and Kazuhiko Ozeki |
ozeki@ice.uec.ac.jp |
5 |
Evaluation of a Japanese Sentence Compression Method Based on Phrase Significance and Inter-Phrase Dependency |
Text - text/topic summarization |
15_Rei_Oguro_abstract.txt |
Jaroslava Hlavacova |
A |
|
Eduard Hovy |
|
|
LP |
15_Rei_Oguro.pdf |
15_Rei_Oguro.ps |
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16 |
Tiit Hennoste |
koit@ut.ee |
6 |
Determining Dialogue Acts in Estonian Dialogue Corpus |
Dialogue - other |
16_Tiit_Hennoste_abstract.txt |
Ivan Kopecek |
W |
|
Vaclav Matousek |
A |
The work isn't original, but the topic seems me to be very useful for the developing former Soviet country. I recommend to give an opportunity to the authors to present this submission at the conference. |
Reject |
16_Tiit_Hennoste.pdf |
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17 |
Michael V. Boldasov and Elena G. Sokolova and Michael G. Malkovsky |
boldasov@nm.ru |
3 |
User query understanding by the InBASE system as a source for a multilingual NLG module(first step) |
Text - multi-lingual issues |
17_Michael_V._Boldasov_abstract.txt |
Steven Krauwer |
A |
|
Elmar Noeth |
A |
interesting work Reconsider the title: the term NLG is not known in the communitiy and not introduced in the paper Could you elaborate a little on users' experiences? The English needs work! (Maybe you have a competent proofreader) stuff should be staff! also anybody, everybody, |
SP |
|
17_Michael_V._Boldasov.ps |
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18 |
Anton Batliner and Viktor Zeissler and Elmar Nöth and Heinrich Niemann |
batliner@informatik.uni-erlangen.de |
4 |
Prosodic Classification of Offtalk: First Experiments |
Dialogue - prosody and emotions in dialogues |
18_Anton_Batliner_abstract.txt |
Ivan Kopecek |
A |
|
Karel Pala |
S |
--- |
LP |
18_Anton_Batliner.pdf |
18_Anton_Batliner.ps |
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20 |
Tomaž Rotovnik and Mirjam Sepesy Maučec and Bogomir Horvat and Zdravko Kačič, |
tomaz.rotovnik@uni-mb.si |
4 |
Large Vocabulary Speech Recognition of Slovenian Language Using Data-Driven Morphological Models |
Speech - automatic speech recognition |
20_Rotovnik_Tomaz_abstract.txt |
Leon Rothkrantz |
A |
-It is a pity that the authors did not submit an extended paper, providing more technical details -In ASP recognition of the "stem"of words is important, ďnflection"can be generated from the context using grammatical rules -Using syllables as modelling units seems to be a natural extension of the proposed method in the tradition of the French schools. But then many subwords models have to be trained and a huge number of data is necessary. This aspect should be discussed in more details -it is not clear why the authors used a reduced phoneme set -the English should be improved , many articles (the, a etc.) are deleted examples section 1 line 1: The Slovenian language is a highly ... line 4 have a complex morphological structure etc. -there is an error is tabele 1, 1665 should be replaced by 165 -in the abstract the authors claim that they got an improvement of 2.5 %, but in the conclusion thewy report about similar results? |
E.G. Schukat-Talamazzini |
A |
|
SP |
20_Rotovnik_Tomaz.pdf |
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21 |
Jinyoung Kim |
kimjin@dsp.chonnam.ac.kr |
2 |
Modified LBG Clustering Algorithms for Small Unit Inventory in Corpus-based TTS system |
Speech - text-to-speech synthesis |
21_Jinyoung_Kim_abstract.txt |
Pavel Skrelin |
W |
|
Taras Vintsiuk |
A |
|
Reject |
21_Jinyoung_Kim.pdf |
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22 |
Manolis Maragoudakis and Aristomenis Thanopoulos and Nikos Fakotakis |
mmarag@wcl.ee.upatras.gr |
3 |
Statistical Decision Making applied to Text and Dialogue Corpora for Effective Plan Recognition |
Dialogue - development of dialogue strategies |
22_Manolis_Maragoudakis_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
accept after removing minor errors in English |
LP |
22_Manolis_Maragoudakis.pdf |
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23 |
Dana Hlavackova |
rsedlac@fi.muni.cz |
2 |
Morphological Tagging of the Brno Spoken Corpus |
Text - text corpora |
23_Dana_Hlavackova_abstract.txt |
Karel Oliva |
W |
It seems to me that there occurs quite a strong tension between the title= of the paper (Morphological tagging ....) and its contents: there is a l= ot of information about the corpus of spoken Czech, but virtually *nothin= g* about how the tagging is performed (and there is no hint even in the R= eferences). The *only* information a reader gets is that your tagging is somewhere be= tween 60% and 70% correct (once you say 60, once 70). But there is nothin= g about the method, about how the results were measured ... Also, I missed any information about your contribution: is it you who col= lected the data ? Is it you who annotated parts of them / invented and te= sted some new tagging method ... You should either change the contents of the paper accordingly (and profo= undly) or change the title, hence. |
Vladimir Petkevic |
W |
The article is very short and not interesting, its is poor in content. It only sketchily describes the whole subject with no interesting ideas presented. The reported results are poor (also due to the complexity of the problem). The approach should have been described in a more detailed way, it should have concentrated on interesting aspects and report the results more thoroughly with the discussion of problems encountered. |
Reject |
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23_Dana_Hlavackova.ps |
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24 |
Marta Gatius and Horacio Rodríguez |
gatius@lsi.upc.es |
2 |
NATURAL LANGUAGE GUIDED DIALOGUES FOR ACCESSING THE WEB |
Dialogue - dialogue systems |
24_Marta_Gatius_abstract.txt |
Elmar Noeth |
A |
Fig. 1 is unreadable (prob. because it is a color picture) I'm missing information about user experiences |
Karel Pala |
A |
|
LP |
24_Marta_Gatius.pdf |
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25 |
Jiří Mírovský |
ondruska@ufal.ms.mff.cuni.cz |
2 |
NetGraph System--Searching through the Prague Dependency Treebank |
Text - text corpora |
25_Jiří_Mírovský_abstract.txt |
Yorick Wilks |
|
|
Dr. Alexander Gelboukh Kahn |
W |
Paper: NetGraph System by Juri Mirovsky et al. Remove running page heads and page numbers. |
Reject |
25_Jiří_Mírovský.pdf |
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26 |
Jindřich Matoušek and Daniel Tihelka and Josef Psutka and Jana Hesová |
jmatouse@kky.zcu.cz |
4 |
German and Czech Speech Synthesis Using HMM-Based Speech Segment Database |
Speech - text-to-speech synthesis |
26_Jindrich_Matousek_abstract.txt |
Hynek Hermansky |
A |
|
Genevieve Baudoin |
S |
|
LP |
26_Jindrich_Matousek.pdf |
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27 |
Markéta Lopatková and Veronika Řezníčková and Zdeněk Žabokrtský, |
zabokrtsky@ckl.mff.cuni.cz |
3 |
Valency Lexicon for Czech: from Verbs to Nouns |
Text - other |
27_Zdenek_Zabokrtsky_abstract.txt |
Eneko Agirre |
S |
Interesting paper, although preliminary. You might be interested in work on the dissambiguation of derivational suffixes and the meaning of the roots (check Penthedourakis, J. and Vanderwende, L., 1993. Automatically Identifying Morphological Relations in Machine-Readable Dictionaries. Microsoft internal report MSR-TR-93-06.). |
Yorick Wilks |
A |
I cannot judge the Czech's but this seems an excellent and well-planned and motivated resource. |
SP |
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27_Zdenek_Zabokrtsky.ps |
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29 |
Georg Stemmer and Stefan Steidl and Elmar Nöth and Heinrich Niemann and Anton Batliner |
stemmer@informatik.uni-erlangen.de |
5 |
Comparison and Combination of Confidence Measures |
Speech - automatic speech recognition |
29_Georg_Stemmer_abstract.txt |
Attila Ferencz |
S |
|
Nikola Pavesic |
A |
|
LP |
29_Georg_Stemmer.pdf |
29_Georg_Stemmer.ps |
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30 |
Andrej Žgank and Tomaž Rotovnik and Zdravko Kačič and Bogomir Horvat |
andrej.zgank@uni-mb.si |
4 |
Uniform Speech Recognition Platform for Evaluation of New Algorithms |
Speech - automatic speech recognition |
30_Andrej_Zgank_abstract.txt |
Josef Psutka |
A |
The uniform platform is a good thing to provide many comparative experiments in spoken Slovenian. The performed tests could be considered as the standard work. |
Leon Rothkrantz |
A |
-the research project should be reported as an extended paper, no a lotof technical details are missing -the architecture could be presented using a Figure with the essential components -it is not clear what is implemented and what and how it can be tested, a lot is reported in general terms -what is an evaluation of implementation"" ??, how evaluation is implemented?? evaluation of the implemented system?? the goal of this section is to show thatb the tool can be used and the results of a testexample are reported? In that case more details should be presented how to use the system -the text has a lot of spelling errors (articles ""ä", "the") examples abstract line 1 the development of a speech recognition platform introduction line 5 is based on the similar ... line 7 for a broad spectrum .... line 10 in the future the port to the ... |
SP |
30_Andrej_Zgank.pdf |
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31 |
Jan Nouza |
jan.nouza@vslib.cz |
1 |
Strategies for Developing a Real-Time Continuous Speech Recognition System for Czech Language |
Speech - automatic speech recognition |
31_Nouza_abstract.txt |
E.G. Schukat-Talamazzini |
A |
|
Pavel Skrelin |
A |
|
LP |
31_Nouza.pdf |
|
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32 |
Jan Nouza and Petr Kolář and Josef Chaloupka |
jan.nouza@vslib.cz |
3 |
Voice Chat with a Virtual Character: The Good Soldier Svejk Case Project |
Dialogue - dialogue systems |
32_Jan_Nouza_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
|
SP |
32_Jan_Nouza.pdf |
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33 |
I. Azzini and T. Giorgino and D. Falavigna and R. Gretter |
falavi@itc.it |
4 |
Application of Spoken Dialogue Technology in a Medical Domain |
Dialogue - dialogue systems |
33_Daniele_Falavigna_abstract.txt |
Elmar Noeth |
S |
Nice work; it would be great to see preliminary results of the field test in the final version of the paper |
Karel Pala |
A |
The meaning of the abbreviation 'ITC-irst' referring to your Institute as far as I can see is not very obvious at the first glance - perhaps it would help to change the title of the paper a little? kp. |
SP |
|
33_Daniele_Falavigna.ps |
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34 |
Goran Nenadić and Irena Spasić and Sophia Ananiadou |
g.nenadic@salford.ac.uk |
3 |
Term Clustering using a Corpus-Based Similarity Measure |
Text - knowledge representation and reasoning |
34_Goran_Nenadic_abstract.txt |
Hanks Patrick |
S |
|
Jaroslava Hlavacova |
S |
Isn't there a (typing) mistake in the fraction denominators in the definitions of CS and LS? Content-Description: 34.pdf |
SP |
34_Goran_Nenadic.pdf |
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35 |
Emilio Sanchis and Fernando García and Isabel Galiano and Encarna Segarra |
esanchis@dsic.upv.es |
4 |
Applying dialogue constraints to the understanding process in a Dialogue system |
Dialogue - dialogue systems |
35_SANCHIS,_EMILIO_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
interesting for presentation, useful for the Spanish country; I recommend to accept this submission and to give to the authors to present this submission at the conference. |
LP |
35_SANCHIS,_EMILIO.pdf |
35_SANCHIS,_EMILIO.ps |
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36 |
Carlos D. Martínez-Hinarejos and Francisco Casacuberta |
cmartine@iti.upv.es |
2 |
Evaluating a Probabilistic Dialogue Model for a Railway Information Task |
Dialogue - other |
36_Carlos_D._Martinez-Hinarejos_abstract.txt |
Elmar Noeth |
S |
Nice work; of course not enough data :) Personally I would cite Searle rather than Fukuda et al. when introducing dialogue acts. There has also been earlier and more profound work on dialogue acts and speech recognition. If I interpret your numbers on page 5 correctly, then in 194 dialogs only 174 system turns were uttered? This does not make sense! Please clarify what number is Type and what is Token Look at: M. Boros, W. Eckert, F. Gallwitz, G. Görz, G. Hanrieder, and H. Niemann. Towards understanding spontaneous speech: Word accuracy vs. Concept accuracy. In Proc. Int. Conf. on Spoken Language Processing, volume 2, pages 1005-1008, Philadelphia, PA, USA, 1996. where - rather than on the speech act level - a system accuracy of a train information system is calculated on the level of semantic entities. May be that is more appropriate. (The paper per se deals with recognition errors, but the definition of concept accuracy might be interesting for you) |
Ivan Kopecek |
A |
|
LP |
36_Carlos_D._Martinez-Hinarejos.pdf |
36_Carlos_D._Martinez-Hinarejos.ps |
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37 |
Dana Nejedlová |
dana.nejedlova@vslib.cz, jan.nouza@vslib.cz |
1 |
Comparative Study on Bigram Language Models for Spoken Czech Recognition |
Speech - automatic speech recognition |
37_Dana_Nejedlova_abstract.txt |
Taras Vintsiuk |
A |
|
Hynek Hermansky |
A |
|
LP |
37_Dana_Nejedlova.pdf |
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38 |
Pascal Wiggers and Leon J. M. Rothkrantz |
p.wiggers@its.tudelft.nl |
2 |
Integration of speech recognition and automatic lipreading |
Speech - automatic speech recognition |
38_Pascal_Wiggers_abstract.txt |
Genevieve Baudoin |
S |
|
Attila Ferencz |
A |
|
LP |
|
38_Pascal_Wiggers.ps |
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39 |
Dimitri Woei-A-Jin |
L.J.M.Rothkrantz@its.tudelft.nl |
2 |
Anaphora Resolution in a speech recognition environment |
Speech - other |
39_Dimitri_Woei-A-Jin_abstract.txt |
Nikola Pavesic |
A |
|
Josef Psutka |
A |
Very nice application. |
will not arrive |
|
39_Dimitri_Woei-A-Jin.ps |
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41 |
Vlasta Radova |
radova@kky.zcu.cz |
2 |
A Method for Segmentation of Voiced Speech Signals into Pitch Period Segments |
Speech - speech segmentation |
41_Vlasta_Radova_abstract.txt |
Leon Rothkrantz |
A |
At this moment there are many methodsproposed for automatic segmentation, but none of them is perfect. In the paper an iteresting new approach is presented. But the paper has to minor points First the authors don't relate their work to the work of others, they even don't mention similar approaches by other (only Vintsiuk) Seconly it would be nice to compare the test results with wellknown segmentationalgorithm, so to implment both and test it on the same corpus Thirdly I don't see why the testresuts are not compared with mauasegmentation. Take for example the TIMIT database which is segmented and apply the new segmentationalgoritm on that database (I presume that the proposed algorthm is language independent on the voiced part of the speech Automatic selection of voiced part ofspechca be realised by autocorrelation methods. |
E.G. Schukat-Talamazzini |
W |
|
Reject |
41_Vlasta_Radova.pdf |
41_Vlasta_Radova.ps |
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42 |
Michal Prcín and Luděk Müller |
mprcin@kky.zcu.cz |
2 |
Heuristic and Statistical Methods for Speech/Non-speech Detector Design |
Speech - automatic speech recognition |
42_Michal_Prcin_abstract.txt |
Pavel Skrelin |
S |
|
Taras Vintsiuk |
W |
|
LP |
|
42_Michal_Prcin.ps |
|
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43 |
Adam Przepiorkowski |
adamp@ipipan.waw.pl |
2 |
The Unbearable Lightness of Tagging: Case Study in Polish Morphology |
Text - parsing and part-of-speech tagging |
43_Adam_Przepiorkowski_abstract.txt |
Eduard Hovy |
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Steven Krauwer |
W |
What should come out of a tagger is solely dependent on the needs of the process that is going to use the result. If that process is not known or not properly defined, the whole discussion becomes pretty empty. |
Reject |
43_Adam_Przepiorkowski.pdf |
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44 |
Nestor Garay-Vitoria and Julio Abascal and Luis Gardeazabal |
nestor@si.ehu.es |
2 |
Evaluation of prediction methods applied to an inflected language |
Dialogue - assistive technologies based on speech and dialogue |
44_Nestor_Garay-Vitoria_abstract.txt |
Ivan Kopecek |
A |
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Vaclav Matousek |
A |
interesting submission; I reccomend to accept it as is. secondly: I reccomed to submit this article to the conference on minor languages too. |
LP |
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44_Nestor_Garay-Vitoria.ps |
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45 |
Andrés Montoyo and Rafael Romero and Sonia Vázquez and Carmen Calle and Susana Soler |
montoyo@dlsi.ua.es |
5 |
The Role of WSD for Multilingual Natural Language Applications |
Text - word sense disambiguation |
45_Andrés_Montoyo_abstract.txt |
Steven Krauwer |
S |
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Karel Oliva |
W |
The paper proposes a new architecture, but contains only very little comparison to other systems approaches. Also, it completely lacks any evaluation (how successful the system is. There are quite some erors in English, in particular in subject-verb agreement (and also some others). |
LP |
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45_Andrés_Montoyo.ps |
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46 |
Maria Yavorskaya |
yav_mas@hotmail.com, asinopalnikova@yahoo.com |
3 |
Wordnet as a Tool for Measurement of Domain Similarity of Texts |
Text - information retrieval |
46_Maria_Yavorskaya_abstract.txt |
Vladimir Petkevic |
R |
The article is but a sketch. It is written in a very negligible and general way. No results have been presented, nothing interesting shown. |
Yorick Wilks |
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Reject |
46_Maria_Yavorskaya.pdf |
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47 |
Antoine Rozenknop |
antoine.rozenknop@epfl.ch |
1 |
A Gibbsian Context-Free Grammar for Parsing |
Text - parsing and part-of-speech tagging |
47_Antoine_Rozenknop_abstract.txt |
Dr. Alexander Gelboukh Kahn |
W |
Paper: A Gibbsian Context-Free... by Antonie Rozenknop My low evaluation of the technical quality of your paper is due to insufficient experimental results: you did not demonstrate that your approach gives good results in the real life situation (Test not= Learn). I am sure your paper would be unconditionally accepted if you presented real evaluation and demonstrated real improvement on the unseen data. Also, your paper definitely needs proof-reading by someone with good knowledge of English. Some words look like written in French (and some even _are_ written in French). Title: consider removing "A": "Gibbsian..." Author: Where is Footnote 1? Address: Suisse = Switzerland? Please use Englihs! Consider the following changes: Abstract: |
Karel Oliva |
S |
The paper puts forward an interesting discussion on an alternative to PCFG, and above all proposes such an alternative. Generally, the submission seems a little bit premature - it woudl be definitely nicer if you were able to give more evaluation as well as could report on the results of the work you say you only plan. Then the paper would be truly excellent. So I consider the contents of the paper very good - what is the problem is the language. If accepted you DEFINITELY have to turn the Franglais into real English !!! On very many places, you use words which are kind of mixed, sometimes you even use pure French (the article "la", should be "the", or captions to Fig 2). |
LP |
47_Antoine_Rozenknop.pdf |
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48 |
Ilyas Potamitis and Nikos Fakotakis and Nikos Liolios and George Kokkinakis |
potamitis@wcl.ee.upatras.gr |
1 |
SPEECH ENHANCEMENT USING MIXTURES OF GAUSSIANS FOR SPEECH AND NOISE |
Speech - other |
48_Potamitis_Ilyas_abstract.txt |
Hynek Hermansky |
A |
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Genevieve Baudoin |
A |
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SP |
48_Potamitis_Ilyas.pdf |
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49 |
Armando Suárez and Manuel Palomar |
armando@dlsi.ua.es |
2 |
Word Sense vs. Word Domain Disambiguation: a Maximum Entropy approach |
Text - word sense disambiguation |
49_ARMANDO_SUAREZ_abstract.txt |
Eva Hajicova |
W |
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Hanks Patrick |
S |
|
LP |
49_ARMANDO_SUAREZ.pdf |
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50 |
César González Ferreras and David Escudero Mancebo and Valentírn Carde\ noso Payo |
cesargf@infor.uva.es |
3 |
From HTML to VoiceXML: A first approach. |
Dialogue - markup languages related to speech and dialogue |
50_Cesar_Gonzalez_Ferreras_abstract.txt |
Elmar Noeth |
S |
nice paper Do you have any user experiences? Please elaborate on chapter 3: how do you construct the FSD. Having in mind that Vxml and your system will be used, can this influence the original design of a web-page? How much hand work |
Karel Pala |
A |
-- kp. |
SP |
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50_Cesar_Gonzalez_Ferreras.ps |
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51 |
Juan Luis Garcia Zapata |
jgzapata@unex.es |
4 |
On Portability of Automatic Speech Recognition: A Study Case |
Speech - automatic speech recognition |
51_Juan_Luis_Garcia_Zapata_abstract.txt |
Attila Ferencz |
W |
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Nikola Pavesic |
W |
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Reject |
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51_Juan_Luis_Garcia_Zapata.ps |
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52 |
Piotr Banski |
bansp@venus.ci.uw.edu.pl |
1 |
The Pros and Cons of Stand-off Annotation: IPI PAN Corpus Design |
Text - text corpora |
52_Piotr_Banski_abstract.txt |
Jaroslava Hlavacova |
W |
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Eduard Hovy |
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Reject |
52_Piotr_Banski.pdf |
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53 |
D.W. Oard and D. Demner-Fushman and J. Hajič and B. Ramabhadran and S. Gustman and W.J. Byrne and D. Soergel and B. Dorr and P. Resnik and M. Picheny |
oard@glue.umd.edu |
10 |
Cross-Language Access to Recorded Speech in the MALACH project |
Text - information retrieval |
53_Douglas_W._Oard_abstract.txt |
Steven Krauwer |
A |
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Leon Rothkrantz |
A |
|
LP |
53_Douglas_W._Oard.pdf |
53_Douglas_W._Oard.ps |
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54 |
Gies Bouwman and Lou Boves |
bouwman@let.kun.nl |
2 |
Utterance Verification based on the Likelihood Distance to Alternative Paths |
Speech - automatic speech recognition |
54_Gies_Bouwman_abstract.txt |
Josef Psutka |
S |
Very nice paper with many original ideas. |
Leon Rothkrantz |
S |
-the authors report about improvements in recognition rate, they also report about two classes of error substitution and insertion for which class they got the best results? -it proves that Cart is better than LC, as can be expected, but no evidence is reported -it is not clear if the corpus consists of isolated words of city names , maybe those names are extracted from sentences of continuous speech recordings -there is a typing error in the introduction line 14 on the based on speech?? |
LP |
54_Gies_Bouwman.pdf |
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55 |
Tomáš Bartoš and Luděk Müller |
tbartos@kky.zcu.cz |
2 |
Rejection technique based on the mumble model |
Speech - other |
55_Tomáš_Bartoš_abstract.txt |
E.G. Schukat-Talamazzini |
W |
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Pavel Skrelin |
S |
|
LP |
55_Tomáš_Bartoš.pdf |
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56 |
Petr Motlírček and Lukáš Burget |
petr@asp.ogi.edu |
2 |
Efficient Noise Estimation and its Application for Robust Speech Recognition |
Speech - automatic speech recognition |
56_Petr_Motlicek_abstract.txt |
Taras Vintsiuk |
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Hynek Hermansky |
A |
|
LP |
56_Petr_Motlicek.pdf |
56_Petr_Motlicek.ps |
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58 |
Milan Sečujski and Radovan Obradović and Darko Pekar and Ljubomir Jovanov and Vlado Delić |
secujski@uns.ns.ac.yu |
1 |
Synthesis in Serbian Language |
Speech - text-to-speech synthesis |
58_Milan_Secujski_abstract.txt |
Genevieve Baudoin |
A |
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Attila Ferencz |
A |
|
LP |
58_Milan_Secujski.pdf |
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59 |
Gregers Koch |
gregers@diku.dk |
1 |
Basic Principles of Automated Information Extraction |
Text - information retrieval |
59_Gregers_Koch_abstract.txt |
Karel Oliva |
R |
To call the paper "BASIC PRINCIPLES of Automated Information Extraction" = seems to be a kind of overkill. =20 The paper in fact proposes to look at (extracting) informational content = as onto a dataflow (Sect. 3). =20 And it proposes the implementation of this dataflow by means of sharing t= he logical variable. =20 I am afraid that such a view is neither particularly original nor complex= enough to take care of the problems which arise in information extractio= n from real texts. In fact, exactly the same concept (aka "semantic repre= sentation") has been proposed already in the first papers presenting Defi= nite Clause Grammars about 20 years ago. Apart from this general view, some minor points: - whether assigning one or several formalized semantic representations is= "An absolutely central problem of semantics" is a matter of personal vie= w. Maybe a slightly less radical formulation might be due. |
Vladimir Petkevic |
R |
The extraction of information content is an extremely complex task and I think your approach, as far I could understand it, can hardly work for only slightly more complex sentences than the one you presented. The overall approach is, to my mind, very naive and can hardly have significance which surpasses the realm of only the simplest sentence structures. I do not claim, however, to be an expert in the area. Your English should be better - for instance, the 1st sentence in the abstract is syntactically wrong. Your presentation, esp. the trees are almost illegible. |
Reject |
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59_Gregers_Koch.ps |
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60 |
Krasimira Petrova |
krasi@slav.uni-sofia.bg |
2 |
Adaptation of Swedish Transcription System for Spoken Language Analysis for Bulgarian |
Speech - other |
60_Krasimira_Petrova_abstract.txt |
Nikola Pavesic |
A |
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Josef Psutka |
R |
This is not a scientific paper but only a short report on a bilateral project "Multimedia ... " without a scientific background. |
Reject |
60_Krasimira_Petrova.pdf |
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61 |
Marek Trabalka and Mária Bieliková |
bielikova@dcs.elf.stuba.sk |
2 |
Using Salient Words to Perform Categorization of Web Sites |
Text - information retrieval |
61_Marek_Trabalka_abstract.txt |
Yorick Wilks |
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Dr. Alexander Gelboukh Kahn |
A |
Paper: Using Salient Words.. by Marek Trabalka et al. Your paper definitely needs proof-reading by someone with good knowledge of English. The use of articles in your text is a disaster! Sometimes it is just unreadable because of wrong use of articles. |
LP |
61_Marek_Trabalka.pdf |
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62 |
Petya N. Osenova |
osenova@slav.uni-sofia.bg |
2 |
automatic Identification and Linguistic Description of the Abbreviation in the BulTreeBank Electronic Text Archive |
Text - parsing and part-of-speech tagging |
62_Petya_N._Osenova_abstract.txt |
Eneko Agirre |
W |
The approach is interesting, but basic. There is no comparison to other work on the area (Park & Byrd, http://www.research.ibm.com/talent/documents/emnlp2001_48.pdf) (Sundaresan & Yi, http://www9.org/w9cdrom/363/363.html), which could be used to improve the system. Section 2 contains too many references. In section 4.2 the statistical criterion is difficult to understand. Better show an example here. Specifically TokPar is not clear. |
Eva Hajicova |
W |
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Reject |
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62_Petya_N._Osenova.ps |
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63 |
Pavel Skrelin |
paul@phonet.lang.pu.ru |
2 |
A Physical Pause as a Sequence of Special Articulation Gestures |
Speech - other |
63_Pavel_Skrelin_abstract.txt |
Leon Rothkrantz |
A |
The authors claim that the results are different from other studies , but those differences can be caused by other reasons such as differences in testmaterial, subjects etc. the results in the tables are not convincing. It is difficult to use statistical test with such a small number of testpersons but then it is clear if the difference is significant or not in an objective way. If we take recordingd of a newsreader the the same results can be expected? -from table 1 and others we conclude that there is a lot of variation between subjects, there is no explanation for that. -it is not clear why pauses are labeled as "psychological -in section 3 the authors report that a great majority ... (how great is great??) -there are many spelling errors in the text (the articles "the: and ä ëxample section 1 line 1 speech recognition system has an algorithm line 5 we know that speakers line 14 boundaries in a Russian text -the paper has no abstract, section 2 is losely connected to the rest of the paper |
E.G. Schukat-Talamazzini |
W |
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Reject |
63_Pavel_Skrelin.pdf |
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64 |
Darko Pekar |
pekard@EUnet.yu |
3 |
ALFANUM SYSTEM FOR CONTINUOUS SPEECH RECOGNITION |
Speech - automatic speech recognition |
64_Darko_Pekar_abstract.txt |
Pavel Skrelin |
A |
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Taras Vintsiuk |
W |
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Reject |
64_Darko_Pekar.pdf |
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66 |
Roman V. Mescheriakov |
mrv@keva.tusur.ru |
3 |
RECOGNITION AND SPEECH SYNTHESIS IN THE DIALOGUE SYSTEMS STRUCTURES |
Dialogue - other |
66_Roman_V._Mescheriakov_abstract.txt |
Ivan Kopecek |
R |
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Vaclav Matousek |
A |
I didn't see this approach; from this viewpoint the article seems me to be original; but the presentation contains a lot of general phrases; is this work really original? For all that I recommend to give to the author an oportunity to present the submission on the conference. |
Reject |
66_Roman_V._Mescheriakov.pdf |
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67 |
Bronius Tamulynas |
bronius@pit.ktu.lt |
1 |
Multilingual Computer-based Communication and Language Processing: Lithuania case |
Text - multi-lingual issues |
67_Bronius_Tamulynas_abstract.txt |
Hanks Patrick |
A |
|
Jaroslava Hlavacova |
R |
If the aim was to describe a general strategy of computer based translation from one language into another, it is too brief, naming just old common points without going into details. If you wanted to tell something about your special experience with one couple of languages - English / Lithuanian, I would expect a factual data. |
Reject |
67_Bronius_Tamulynas.pdf |
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68 |
Nira B. Volskaya |
nina@PS1098.spb.edu |
1 |
Pause duration at syntactic boundaries |
Speech - other |
68_Nira_B._Volskaya_abstract.txt |
Hynek Hermansky |
A |
|
Genevieve Baudoin |
A |
|
will not arrive |
68_Nira_B._Volskaya.pdf |
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69 |
Huang Ke, Ma Shaoping |
xmirage99@mails.tsinghua.edu.cn |
2 |
Text Categorization Based On Concept Indexing and Principal Component Analysis |
Text - other |
69_Huang_Ke,_Ma_Shaoping__abstract.txt |
Eduard Hovy |
|
|
Steven Krauwer |
A |
|
will not arrive |
69_Huang_Ke,_Ma_Shaoping_.pdf |
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70 |
Gábor Alberti and Helga M. Szabó |
albi@btk.pte.hu |
2 |
Discourse-Semantic Analysis of Hungarian Sign Language |
Text - lexical semantics and semantic networks |
70_GÁBOR_ALBERTI__abstract.txt |
Eva Hajicova |
S |
A very nice paper, focussing on an issue that is not that often discussed at this type of conference but that deserves full attention. It would be also interesting to see how the information-structure analysis (topic-focus articulation) would enrich the DRS's for this application. |
Karel Oliva |
W |
First, the paper does not conform to the required format (e.g., there is no Abstract). Further, it seems to be kind of "torn out" of a larger paper, in fact being something like the first half of a paper. It has no evaluation and worse even no comparison to other methods, no conclusion section ... You should make the paper look like a stand-alone, complete work. |
LP |
70_GÁBOR_ALBERTI_.pdf |
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71 |
Janez Žibert and France Mihelič and Nikola Pavešić |
janez.zibert@fe.uni-lj.si |
3 |
Speech Features Extraction Using Cone-shaped Kernel Distribution |
Speech - automatic speech recognition |
71_Janez_Zibert_abstract.txt |
Attila Ferencz |
A |
|
Leon Rothkrantz |
S |
-The topic of the paper is very interesting but it is in the borderline of TSD, but fits better in ICASS or related signal processing cnferences -In fig 1a and 1b authors compare the results of spectrogram analysis and CKD. But as is noticed the spectrogramresults could be better if the gal is to analyse frequencies (instad of better time resolution. In fig b CKD is optimised for frequency analysis, so the results are very good. In the last section it proves that CKD is etter i recognition of vowels (voiced part of speech) instead of SPEC. We expect that spectogramanalysis would be better on the voiced part of the speech. We also hoped that CKD was better on the nonvoiced part of the speech because common techniques are far from optimal in that area. The kernel fuction approach is related to the "wavelet"-approach but that is not mentioned in the paper |
LP |
71_Janez_Zibert.pdf |
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72 |
Simon Dobrišek and Jerneja Gros and Boštjan Vesnicer and France Mihelič and Nikola Pavešić |
simond@fe.uni-lj.si |
5 |
A Voice-Driven Web Browser for Blind People |
Dialogue - dialogue systems |
72_Simon_Dobrisek_abstract.txt |
Elmar Noeth |
S |
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Karel Pala |
W |
The information given about the particular components of the described browser is rather general, it should be a bit more specific. kp. |
LP |
72_Simon_Dobrisek.pdf |
72_Simon_Dobrisek.ps |
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74 |
Josef Psutka and Pavel Ircing and Josef V. Psutka and Vlasta Radová and William J. Byrne and Jan Hajič and Samuel Gustman and Bhuvana Ramabhadran |
psutka@kky.zcu.cz |
8 |
Automatic Transcription of Czech Language Oral History in the MALACH Project: Resources and Initial Experiments |
Speech - automatic speech recognition |
74_Josef_Psutka_abstract.txt |
Hynek Hermansky |
A |
|
Leon Rothkrantz |
S |
-The paper reports about a very interesting research project -in section 5.1 is reported that acoustic models are trained, but the training data is not mentioned and described at the end of section 5 it is concluded that the recognition rates are weak if we compare it to the results of ASR trained on Broadcast news. This is not suprisingly because the Shoah corpus is composed of spontaneous speech while the Broadcast corpus is composed of og grammatically correct text-speech recordings |
LP |
74_Josef_Psutka.pdf |
74_Josef_Psutka.ps |
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76 |
Antanas LIPEIKA |
lipeika@ktl.mii.lt |
3 |
On Speaker Adaptation in Isolated Word Recognition |
Speech - automatic speech recognition |
76_Antanas_LIPEIKA_abstract.txt |
E.G. Schukat-Talamazzini |
R |
|
Pavel Skrelin |
R |
|
Reject |
76_Antanas_LIPEIKA.pdf |
76_Antanas_LIPEIKA.ps |
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77 |
Daniel Martins |
martins-daniel@wanadoo.fr |
2 |
Influence of text coherence’s disruption on story memorisation and interestingness |
Text - other |
77_Daniel_Martins_abstract.txt |
Vladimir Petkevic |
R |
The results confirm what is crystal clear anyway - so why to perform the research? Really, the topic is definitely not interesting, there are no ideas therein. The presentation and English are both extremely bad - you should have at least spell check the text, the errors are horrible. Next time, you should devote much more time to the preparation and to the choice of the topic of the article before sending it to any kind of conference. |
Yorick Wilks |
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Reject |
77_Daniel_Martins.pdf |
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78 |
Takafusa Kitazume |
sugiyama@u-aizu.ac.jp |
2 |
Automatic Video Caption Generation for Sound Containing Voice and Music |
Speech - speech segmentation |
78_Takafusa_Kitazume_abstract.txt |
Taras Vintsiuk |
W |
|
Hynek Hermansky |
A |
|
Reject |
|
78_Takafusa_Kitazume.ps |
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79 |
Juan M. Montero |
juancho@die.upm.es |
2 |
ANESTTE: a Writer’s Assistant for a Specific Purpose Language |
Text - other |
79_Juan_M._Montero_abstract.txt |
Dr. Alexander Gelboukh Kahn |
W |
Paper: ANESTTE, by Juan M. Montero et al. You should format your paper according to Springer requirements. Your paper does not present an adequate testing. Is this tool available publicly or commercially? How to get it (please give a URL)? Minor language problems: |
Eneko Agirre |
R |
The authors use a template based grammar to measure some stilystic features of technical text. The aplication is interesting. It is not clear if it also adresses grammar errors. The main weaknesses are that the paper is difficult to follow, that there is no mention to other research papers in the area, or comparison to other systems (e.g. the Word grammatical . Besides de evaluation is very weak. For instance why wasn't it evaluated with technical documents vs. other kind of documents? There is no mention to language resources (e.g. dictionary) or algorithms (PoS tagging?) have been used. |
Reject |
79_Juan_M._Montero.pdf |
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80 |
Robert Král |
rkral@fi.muni.cz |
1 |
Word Sense Discrimination for Czech |
Text - word sense disambiguation |
80_Robert_Kral_abstract.txt |
Eva Hajicova |
A |
This would be a nice piece of work if (a) formulated clearly and, first of all, understandably, (b) the approach is illustrated on more than a single example, and (c) on the basis of such a more substantive analysis the evaluation is made. The paper as it is now just makes an impression that the author has taken over an algorithm, applied it to a single ambiguous Czech word (which is not well comparable with the original tests) and has drawn conclusions from that. |
Hanks Patrick |
S |
|
SP |
80_Robert_Kral.pdf |
80_Robert_Kral.ps |
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81 |
Irina Rozina |
rozin@orbita1.ru |
3 |
Interactive learning media for language, communication and culture study |
Dialogue - other |
81_Irina_Rozina_abstract.txt |
Ivan Kopecek |
R |
In my opinion, this is not a research paper. |
Vaclav Matousek |
W |
bad submission format, very brief presentation, I recommend to submit it to other conference or to revise this submission. I mean, you send us the contribution for other conference. Secondly - it is no full paper, it is the extended abstract. |
Reject |
81_Irina_Rozina.pdf |
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82 |
Sorin Dusan |
sdusan@caip.rutgers.edu |
2 |
A System for Multimodal Language Acquisition |
Dialogue - user modeling |
82_Sorin_Dusan_abstract.txt |
Elmar Noeth |
A |
I have trouble with your OOV handling. How come the computer recognizes an unknown word rather than producing a wrong hypothesis? Please elaborate Isn't the 3rd paragraph of chapter 2 and the beginning of chapter 3 the same? I'm missing experimental results - a small field test is better than none |
Karel Pala |
W |
Some parts of the text on p.2, Sect.2, par.3 and p.3 Sect.3, par.1 are repeating, obviously the technique "cut and paste" was used - this can be hardly accepted. The description of the grammar appears to be quite simple and rather superficial, number of the grammar rules is really small. The semantic database mentioned in the paper does not seem to be related to any of the known knowledge represention language judging from the given Bibliography, thus it is ad hoc solution?. kp. |
Reject |
82_Sorin_Dusan.pdf |
82_Sorin_Dusan.ps |
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84 |
Takeshi Akatsuka |
m5051101@u-aizu.ac.jp |
2 |
Automatic Generation of Iroha-Uta Poetry |
Text - other |
84_Takeshi_Akatsuka_abstract.txt |
Jaroslava Hlavacova |
R |
Not well explained. There are undefined symbols, or they are introduced later than they were used in the paper. What is "more meaningful" from the Conclusion? I can't imagine, how the using of the 2nd algorithm could be (fully) meaningful. The paper describes a japanese (sort of) game with words and even if it is interesting, I can't see its usefulness for other languages. |
Eduard Hovy |
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|
Reject |
84_Takeshi_Akatsuka.pdf |
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85 |
Lim Tek Yong |
tylim@cs.usm.my |
2 |
The Exploratory of Personal Assistants |
Dialogue - user modeling |
85_Lim_Tek_Yong_abstract.txt |
Ivan Kopecek |
W |
|
Vaclav Matousek |
W |
The contribution is nice prepared, but it deals with common (general) problems of the HCI. Therefore the relevance to TSD conference isn't high; I recommend to contribute this paper to the INTERACT 2003 conference. |
Reject |
85_Lim_Tek_Yong.pdf |
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86 |
Dita Bartůšková and Radek Sedláček |
rsedlac@fi.muni.cz |
2 |
Tools for Semi-Automatic Assignment of Czech Nouns to Declination Patterns |
Text - automatic morphology |
86_Dita_Bartuskova_abstract.txt |
Steven Krauwer |
W |
|
Yorick Wilks |
S |
This seems an excellent, up to date (i.e. ML), approach to a clear and precise problem. I cannot judge the Czech language issues of course. |
SP |
|
86_Dita_Bartuskova.ps |
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87 |
Tomáš Holan |
holan@ksvi.ms.mff.cuni.cz |
1 |
Dependency Analyser Configurable by Measures |
Text - parsing and part-of-speech tagging |
87_Tomas_Holan_abstract.txt |
Karel Oliva |
S |
*IMPROVE* your English: |
Vladimir Petkevic |
S |
Nice paper with a promising content. The type of analyzer seems to be especially appropriate for languages with the high degree of word-order. I have only one critical remark: the presentation written in English contains many errors (some of them could have been corrected by means of a grammar-checker, for example!) which must definitely be corrected when you will be preparing the final version of the paper. Devote much care to the final preparation of the paper, please. |
LP |
87_Tomas_Holan.pdf |
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88 |
C.K. Yang and L.J.M. Rothkrantz |
L.J.M.Rothkrantz@its.tudelft.nl |
2 |
knowledge based speech interface for handhelds |
Dialogue - development of dialogue strategies |
88_Cheng-KeYang_abstract.txt |
Elmar Noeth |
S |
I like the paper very much. Nevertheless I don't see an indication of the advantage for a system designer in the paper. Why would I want to use your your system? Where do I save work? In my printout the ` and ' came out wrong. |
Karel Pala |
W |
unfortunately very little is said about the handhelds in the paper, thus there is a conflict between the title and the rest of the paper and in this sense the paper is not complete. The authors should amend that, othervise the paper can be hardly accepted. kp. |
LP |
|
88_Cheng-KeYang.ps |
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89 |
Pavel Cenek |
xcenek@fi.muni.cz |
1 |
A Flexible Framework for Evaluation of New Algorithms for Dialogue Systems |
Dialogue - dialogue systems |
89_Pavel_Cenek_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
nice prepared contribution, original topic, accept without comments. |
SP |
89_Pavel_Cenek.pdf |
89_Pavel_Cenek.ps |
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|
90 |
Sven Hartrumpf and Hermann Helbig |
sven.hartrumpf@fernuni-hagen.de |
2 |
The Generation and Use of Layer Information in Multilayered Extended Semantic Networks |
Text - lexical semantics and semantic networks |
90_Sven_Hartrumpf_abstract.txt |
Yorick Wilks |
|
|
Dr. Alexander Gelboukh Kahn |
S |
Paper: The Generation... by H. Helbig Needs to be formatted as Springer requires. |
LP |
90_Sven_Hartrumpf.pdf |
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91 |
Zervas P. and Potamitis I. and Fakotakis N. and Kokkinakis G. |
pzervas@wcl.ee.upatras.gr |
4 |
ON THE FIRST GREEK-TTS BASED ON FESTIVAL SPEECH SYNTHESIS: ARCHITECTURE AND COMPONENTS DESCRIPTION |
Speech - text-to-speech synthesis |
91_Zervas_Panos_abstract.txt |
Genevieve Baudoin |
A |
|
Attila Ferencz |
A |
|
SP |
91_Zervas_Panos.pdf |
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92 |
Antonio Molina |
amolina@dsic.upv.es |
3 |
A Hidden Markov Model Approach to Word Sense Disambiguation |
Text - word sense disambiguation |
92_Antonio_Molina_abstract.txt |
Eneko Agirre |
W |
NOTE: This paper applies a previously developped method (LREC 2002) to a different test set: senseval 2 all-words. The LREC paper is not yet available but from the authors comments it can be assumed that the only difference is that of the evaluation, without further development. If that is not the case, the authors should describe better which are the improvements of their system with respect to the other publication. |
Eva Hajicova |
R |
|
Reject |
92_Antonio_Molina.pdf |
92_Antonio_Molina.ps |
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93 |
Aleš Horák and Vladimírr Kadlec and Pavel Smrž |
hales@fi.muni.cz |
3 |
Enhancing Best Analysis Selection and Parser Comparison |
Text - parsing and part-of-speech tagging |
93_Ales_Horak_abstract.txt |
Hanks Patrick |
A |
|
Jaroslava Hlavacova |
A |
What is PDTB? |
LP |
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93_Ales_Horak.ps |
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94 |
Fatiha Sadat and Masatoshi Yoshikawa and Shunsuke Uemura |
fatia-s@is.aist-nara.ac.jp |
3 |
Exploiting Thesauri and Hierarchical Categories in Cross-Language Information Retrieval |
Text - information retrieval |
94_Fatiha_SADAT_abstract.txt |
Eduard Hovy |
|
|
Steven Krauwer |
A |
|
LP |
94_Fatiha_SADAT.pdf |
94_Fatiha_SADAT.ps |
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95 |
Francisco Díaz |
fdiaz@almendro.datsi.fi.upm.es |
5 |
Segmentation of TI-Digits Corpus with Hidden Markov Models |
Speech - speech segmentation |
95_Francisco_Díaz_abstract.txt |
Nikola Pavesic |
A |
|
Josef Psutka |
W |
This paper does not bring any new ideas and results. |
Reject |
95_Francisco_Díaz.pdf |
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96 |
Csaba Szepesvari |
szepes@mindmaker.hu |
1 |
On the utility of smoothing in spoken dialogue systems |
Dialogue - dialogue systems |
96_Csaba_Szepesvari_abstract.txt |
Elmar Noeth |
W |
What is r.h.s. (page V)? eg. and ie. is written as e.g. and i.e. unresolved citation on p. VI I'm sorry, but I have trouble judging what - if any - has been implemented and experimentally been verified |
Karel Pala |
W |
|
Reject |
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96_Csaba_Szepesvari.ps |
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97 |
Robert Batůšek |
xbatusek@fi.muni.cz |
1 |
An Analysis of Limited Domains for Speech Synthesis |
Speech - text-to-speech synthesis |
97_Robert_Batusek_abstract.txt |
Leon Rothkrantz |
A |
-The paper is (too) short, a lot of aspects should be described in more details, i.e. in the section feature generation the authors give an indication which featues are used. But there should be a list of features. These list is used in the experiments so these details should be available. Maybe the lisit is too long but part of it should be presented. -Secton 3 starts with "all experiments have been made....", the question is which experiments? -In section 4 the authors state that the list of possible features is practically infinite. But all features are equal but some of them are more equal. So maybe it is possible to compute frequencies of the features and do the experiments with the most important features. It is also interesting to know the distribution of the feature set over the different corpora and the different testset. Maybe there will be a great variation -in table 1 some features are listed but maybe a little bit criptic for the reader who has no access to he former papers of the authors. So maybe some additional information can be provided and also some details of the Demosthenes speech synthesiser. -Maybe the authors have to consider even more restricted corpora. The corpus of dialogues of train-timetables is even more limited so maybe the featues. If we reduce the corpus maybe not all the names of railwaystations are represented but maybe all time-expressons are represented. So the frequency of the features and the relation to specific topics may be important Content-type: application/octet-stream; name="97.pdf"; type=Unknown; Content-description: 97.pdf Content-disposition: attachment Attachment converted: Gigi:97.pdf (PDF /CARO) (0000FCB9) |
E.G. Schukat-Talamazzini |
A |
|
SP |
97_Robert_Batusek.pdf |
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98 |
Genevieve Baudoin and François Capman and Jan Černocký and Fadi El Chami and Maurice Charbit and Gérard Chollet and Dijana Petrovska-Delacrétaz |
cernocky@fit.vutbr.cz |
7 |
Advances in Very Low Bit Rate Speech Coding using Recognition and Synthesis Techniques |
Speech - speech coding |
98_Dijana_Petrovska-Delacretaz_abstract.txt |
Pavel Skrelin |
A |
|
Taras Vintsiuk |
|
|
LP |
98_Dijana_Petrovska-Delacretaz.pdf |
98_Dijana_Petrovska-Delacretaz.ps |
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99 |
Marion Mast and Thomas Ross and Henrik Schulz and Heli Harrikari |
mmast@de.ibm.com |
4 |
Different Approaches to Build Multilingual Conversational Systems |
Dialogue - dialogue systems |
99_Marion_Mast_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
The contribution is original and from the viewpoint of the topic is carefully prepared. But, it doesn't keep the given contribution format (LNCS); it seems me, that this paper was contributed to another conference or workshop more. Therefore I recommend to take this fact into account by decision about the acceptance of this paper. |
LP |
99_Marion_Mast.pdf |
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100 |
Victoria Arranz and Núria Castell and Montserrat Civit |
varranz@talp.upc.es |
3 |
Strategies to Overcome Problematic Input in a Spanish Dialogue System |
Dialogue - dialogue systems |
100_Victoria_Arranz_abstract.txt |
Elmar Noeth |
S |
|
Karel Pala |
A |
|
LP |
100_Victoria_Arranz.pdf |
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101 |
Robert Hecht and Jürgen Riedler and Gerhard Backfried |
robert.hecht@sail-technology.com |
3 |
Fitting German into N-Gram Language Models |
Speech - automatic speech recognition |
101_Robert_Hecht_abstract.txt |
Hynek Hermansky |
A |
|
Genevieve Baudoin |
A |
|
LP |
|
101_Robert_Hecht.ps |
|
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102 |
Guy Camilleri |
camiller@irit.fr |
1 |
Dialogue systems and planning |
Dialogue - other |
102_Guy_Camilleri_abstract.txt |
Ivan Kopecek |
A |
|
Vaclav Matousek |
A |
interesting contribution, I recommend to accept it for the conference |
LP |
102_Guy_Camilleri.pdf |
|
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103 |
Kris Demuynck and Tom Laureys |
Kris.Demuynck@esat.kuleuven.ac.be |
2 |
A Comparison of Different Approaches to Automatic Speech Segmentation |
Speech - speech segmentation |
103_Kris_Demuynck_abstract.txt |
Attila Ferencz |
A |
|
Nikola Pavesic |
A |
|
LP |
|
103_Kris_Demuynck.ps |
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104 |
Jan Žižka and Aleš Bourek |
zizka@informatics.muni.cz |
2 |
Filtering of Large Numbers of Unstructured Text Documents by the Developed Tool TEA |
Text - information retrieval |
104_Jan_Zizka_abstract.txt |
Vladimir Petkevic |
S |
|
Karel Oliva |
A |
the paper seems to be a nice description of a working system, however (at= least as to the contents of the paper), the system is hardly more than an implementation of known approaches and = techniques; hence the scientific contribution of the paper seems to be ra= ther low. I would recommend presenting the paper as a poster rather than as a full = paper. |
LP |
104_Jan_Zizka.pdf |
104_Jan_Zizka.ps |
|
|
|
105 |
Stefan Grocholewski |
stefan.grocholewski@cs.put.poznan.pl |
1 |
Within-vowels correlation in speech and speaker recognition |
Speech - speaker identification and verification |
105_Stefan_Grocholewski_abstract.txt |
Josef Psutka |
W |
To improve English. |
Leon Rothkrantz |
A |
-The paper is not easy to read , there are a lot of loosely connected statements, the structure should be improved -the topic is not clear, do the authors report about speaker recognition or speaker verification? -the authors consider only vowels, but how are these vowels extracted from speech recordings, what is the error rate -it seams more natural to consider voiced as well as unvoiced parts of speech, so why do the authors restrict to vowels? no there is no proof the they got better results compared to the common methods -in the text the concepts A-B are very confusing, in Fig 1 it ids about speaker A-B in Fig 5,6 it is about databes A-B |
Reject |
105_Stefan_Grocholewski.pdf |
|
|
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|
106 |
Yassine Ben Ayed and Dominique Fohr and Jean Paul Haton and Gérard Chollet |
ybenayed@loria.fr |
4 |
KEYWORD SPOTTING USING SUPPORT VECTOR MACHINES |
Speech - automatic speech recognition |
106_BEN_AYED_abstract.txt |
E.G. Schukat-Talamazzini |
W |
|
Pavel Skrelin |
S |
|
LP |
|
106_BEN_AYED.ps |
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107 |
Guido Aversano and Anna Esposito |
aversano@tin.it |
2 |
Improved performances and automatic parameter estimation for a context-independent speech segmentation algorithm |
Speech - speech segmentation |
107_Guido_Aversano_abstract.txt |
Taras Vintsiuk |
|
|
Hynek Hermansky |
A |
|
LP |
107_Guido_Aversano.pdf |
107_Guido_Aversano.ps |
|
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108 |
Marek Labuzek |
labuzek@ci.pwr.wroc.pl |
2 |
English Translator - A Bi-directional Polish-English Translation System |
Text - machine translation |
108_Marek_Labuzek_abstract.txt |
Vladimir Petkevic |
R |
The task of machine translation is an extremely difficult. All modules must be next to perfect to perform the task in a relatively satisfactory way. Your description is very general and the whole approach is very naive. For instance, if your POS tagger within analysis of the input language will be bad you can hardly get good results on output. If the quality of the parser is, as you say, very unsatisfactory, why do you describe the whole system because it simply can't work. Some results are presented but they are very bad (eg. 86 % tagger accuracy). The whole method is generally relatively sound but the individual components seem to be designed in a very naive way. For Polish, parsing must be performed with VERY DEEP linguistic insights into the structure of such a syntactically complicated language as Polish. |
Yorick Wilks |
|
|
Reject |
108_Marek_Labuzek.pdf |
108_Marek_Labuzek.ps |
|
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|
109 |
Zdenek Svoboda |
zdenek.svoboda@centrum.cz |
1 |
The Encyclopedia Expert |
Text - information retrieval |
109_Zdenek_Svoboda_abstract.txt |
Dr. Alexander Gelboukh Kahn |
W |
Paper: "The Encyclopedia Expert" by Zdenek Svoboda. Format: remove page numbers. Abstract: 1-2 more lines of details on your method (use of XML, etc.). Give the reader an idea of why it is worth effort to read your paper. |
Eneko Agirre |
R |
This paper presents a Q/A system developped following some well-known ideas. It makes more for a demo than a research paper. It does not present innovative work and the linguistic modelling is limited to pattern matching. |
Reject |
109_Zdenek_Svoboda.pdf |
|
|
|
|
110 |
Pascal Nocera and Georges Linares and Dominique Massonié and Loic Lefort |
pascal.nocera@lia.univ-avignon.fr |
4 |
Phoneme Lattice Based A* Search Algorithm for Speech Recognition |
Speech - automatic speech recognition |
110_Pascal_NOCERA_abstract.txt |
Genevieve Baudoin |
S |
|
Attila Ferencz |
A |
|
LP |
|
110_Pascal_NOCERA.ps |
|
|
|
111 |
Christophe Heintz |
christopheheintz@yahoo.com |
1 |
Naming and the management of social interactions |
Dialogue - other |
111_Christophe_Heintz_abstract.txt |
Elmar Noeth |
A |
Interesting paper I feel that it is not clear what kind of human-machine interaction - if any - you have in mind: human-machine interaction to support a theory of human learning, a conversational smalltalk agent that will learn meaning to pass a Turing test (what for?) or practical real-life speech applications? Each of these goals has a right to exist, but I would like to know which one you mean. |
Karel Pala |
R |
Bad English, grammatical errors in agreement: The first sentence in the Introduction: "Development ... lead(s?) to think ... 6th line in Introduction: "word comprehension and production are handle(d?)... The paper is just an interesting essay, however, it does not seem to offer any applicable results that could be somehow exploited in the field of NLP. In my opinion, the paper is not suitable for TSD Conference. kp. |
Reject |
|
111_Christophe_Heintz.ps |
|
|
|
112 |
P. Matějka and P. Schwarz and M. Karafiát and J. Černocký |
cernocky@fit.vutbr.cz |
4 |
Some like it Gaussian ... |
Speech - automatic speech recognition |
112_Pavel_Matejka_abstract.txt |
Nikola Pavesic |
A |
|
Josef Psutka |
A |
Nice paper. But, it is not clear whether all experiments were performed on the HTK toolkit or some other software tool? Also memory requirements and processing time necessary for common technique (baseline system) in comparison with technique based on gaussianization could be mentioned. |
SP |
112_Pavel_Matejka.pdf |
112_Pavel_Matejka.ps |
|
|
|
113 |
Dominic Widdows and Scott Cederberg and Beate Dorow |
dwiddows@csli.stanford.edu |
3 |
Visualisation Techniques for Analysing Meaning |
Text - lexical semantics and semantic networks |
113_Dominic_Widdows_abstract.txt |
Eva Hajicova |
A |
The tools you describe are interesting and useful, especially for showing important connections that would not be seen from the data as such. A graphical remark: the example in Secti 3 should certainly be oplace in some other position in the text. It is also not convincing enough if you say that the top neighbours of the word could be determined by the users - will they have some tool for that? A list of possibilities? A mssing verb in the second sentence of the paragraph starying with: The |
Hanks Patrick |
S |
|
LP |
|
113_Dominic_Widdows.ps |
|
|
|
114 |
Aldezabal |
jibatsaa@si.ehu.es |
5 |
Learning Argument/Adjanct distinction for Basque Verbs |
Text - other |
114_Aldezabal_abstract.txt |
Jaroslava Hlavacova |
A |
|
Eduard Hovy |
|
|
will not arrive |
|
114_Aldezabal.ps |
|
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115 |
Liang Huang and Yinan Peng and Huan Wang and Zhenyu Wu |
blhuang@online.sh.cn |
3 |
Part-of-Speech Tagging for Old Chinese |
Text - parsing and part-of-speech tagging |
115_Liang_HUANG_abstract.txt |
Steven Krauwer |
S |
very original and interesting! |
Karel Oliva |
A |
The contribution of the paper is to be found - apart from dealing with Cl= assical Chinese - mainly in two areas 1. in proposing a tagset for Classical Chinese 2. in developing a statistical tagger for Classical Chinese. Apart form that, you also report creating a small tagged corpus of Classi= cal Chinese. To start with the last, I am slightly afraid that a training corpus of 1.= 000 words (i.e. about 50 sentences) is really TOO small to provide any si= gnificant results. The same holds for your test corpus - 200 words, i.e. about 10 sentences. I dare say that no really representative results can be achieved with thi= s size of corpora. Aport form that, you say you "present simple-yet-effective methods to han= dle the problems (which occur due to the difference of Classical Chinese = from Indoeropean languages)". These methods, however, as a rule turn to be nothing novel but just simpl= ification of the standardly used techniques (e.g., you use simple bigrams= instead of smoothing trigrams). In this respect, it seems that the paper poses interesting problems but d= oes not present clear answers, neither in theory nor in technology, For these reasons, I would propose the paper (if accepted) to be presente= d rather as a poster than as a full paper. |
LP |
115_Liang_HUANG.pdf |
|
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|
|
116 |
Gregory Martynenko |
gymart@ts4306.sbp.edu |
1 |
Statistical Taxonomization of Literary Corpus |
Text - text corpora |
116_Gregory_Martynenko_abstract.txt |
Karel Oliva |
A |
It would be nice to see some more evaluation - in the sense of numbers. E= .g., it would be nice to see in numbers (i.e. QUANTITATIVELY) why some au= thors are nearer to each other than others. Just add some tables or so. *IMPROVE* your English for the publication (if accepted): - generally, commata (you put them where they would stay in Russian, but = English has different rules) |
Vladimir Petkevic |
W |
The article seems to be interesting but too general. It would be adequate to depict some of the techniques in a more detailed way. However, I am not sure that the topic is crucially relevant for the conference. Some (statictical) results and techniques and at least a slightly (I know that the limit is 8 pages at most!) deeper analysis of a certain literary period should also have been shown instead of 2 pages consisting of significant Russian adjectives and nouns. |
Reject |
|
116_Gregory_Martynenko.ps |
|
|
|
117 |
Marina Lublinskaya and Tatiana Sherstinova |
tanya@ts4306.sbp.edu |
2 |
Audio Collections of Endangered Arctic Languages in the Russian Federation |
Speech - other |
117_Tatiana_Sherstinova_abstract.txt |
Leon Rothkrantz |
A |
-the paper is about an interesting problem -the paper is written as a report/essay, no technical details are reported, it is difficult to repeat the study in similar cases -the authors state that they solved a lot of technical problems but not how -the authors report that the studied languages have no official spelling, but how did they solve that problem -the paper has some spelling errors: section 2, line 1 Nenets is the small group of people section 2, third paragraph bothe Nenets and Nganasa languages belong section 2 fourth paragraph their children to have better life conditions section 2 fourth paragraph in most of the cases The Netherlands on the Web section 5 fourth paragraph The speech corpus section 6 line 12 Now they are students |
E.G. Schukat-Talamazzini |
A |
|
LP |
|
117_Tatiana_Sherstinova.ps |
|
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|
118 |
Junko Araki |
jun3@is.s.u-tokyo.ac.jp |
4 |
Action Vectors and Its Application to Interactive Dialogue Systems |
Dialogue - development of dialogue strategies |
118_Junko_Araki_abstract.txt |
Ivan Kopecek |
W |
|
Vaclav Matousek |
A |
The paper describes interesting application, but it doesn't keep the given paper format (LNCS). I recommend to accept the paper as the poster, but after the revision. |
Reject |
118_Junko_Araki.pdf |
118_Junko_Araki.ps |
|
|
|
119 |
Ewa Lukasik |
lukasik@put.poznan.pl |
1 |
Elements of speaker variability in some voiceless phonemes |
Speech - speaker identification and verification |
119_Ewa_Lukasik_abstract.txt |
Pavel Skrelin |
W |
|
Taras Vintsiuk |
A |
|
Reject |
119_Ewa_Lukasik.pdf |
|
|
|
|
120 |
Igor A. Bolshakov |
gelbukh@cic.ipn.mx |
2 |
Word Combinations as an Important Part of Modern Electronic Dictionaries |
Text - lexical semantics and semantic networks |
120_Igor_A._Bolshakov_abstract.txt |
Ivan Kopecek |
R |
|
Eneko Agirre |
R |
The goal of the paper is not clearly stated. The writing is rather awkward, making use of the special terminology used by Melcuk. If I understood the main point is to argue that some hand labor is required to code idioms, terms and collocations in a lexicon. A fact that nobody can argue. It seems that the authors also claim that it is a feasible task, but no hard data is provided. |
Reject |
|
120_Igor_A._Bolshakov.ps |
|
|
|
121 |
Alexander Gelbukh |
gelbukh@cic.ipn.mx |
2 |
A Method for Development of Automatic Morphological Analysis Systems for Inflective Languages |
Text - automatic morphology |
121_Alexander_Gelbukh_abstract.txt |
Eneko Agirre |
W |
Interesting method for avoiding problematic morphological analysis. Unfortunately there is no evaluation of the method: does it work 100% correct for known words? 100% for unknown words? How does it compare to other systems for russian? Other comments: - section 2: an example of stem alternation should be provided at the beginning - section 2.5: the example for stopping is not very clear. In step 3 you check the potential stem stopp in the dictionary. Is stopp in the dictionary? Is stopp one of those stem alternations generated in the generation phase? |
Eva Hajicova |
W |
The paper makes an impression that the authros are not acquainted with the more recent literature on the subject matter, and in addition, they do not make the right sense of the literature they do know (at least that they quote). Analysis by synthesis make be 'recent' in AI literature, but is very traditional n computational linguistics. In the domai the authors want to apply this approach, I am afraid that it might bring an undesirable increase of processing time. Remark to point 2 in Sect. 2.5: there may be more than one set of grammemes for the mentioned inflectional ending (-ing: writing may be a noun or a verb, or at least two verbal forms: nominalization and verbal participle) .One positive thing is the extensive use of the most invaluable Zaliznjak's dictionary. |
Reject |
|
121_Alexander_Gelbukh.ps |
|
|
|
122 |
Rodolfo A. Pazos R. and Alexander Gelbukh and J. Javier González B. and Erika Alarcón R. and Alejandro Mendoza M. and A. Patricia Domírnguez S |
gelbukh@cic.ipn.mx |
6 |
Spanish Natural Language Interface for a Relational Database Querying System |
Text - other |
122_Rodolfo_A._Pazos_R._abstract.txt |
Hanks Patrick |
A |
|
Jaroslava Hlavacova |
A |
|
LP |
|
122_Rodolfo_A._Pazos_R..ps |
|
|
|
123 |
Marek Veber |
mara@fi.muni.cz |
1 |
Formal system for collocations in Czech |
Text - automatic morphology |
110_Marek_Veber_abstract.txt |
Eduard Hovy |
|
|
Steven Krauwer |
R |
I don't think one should embark on any activity with respect to collocations without having looked at standard sources such as Melcuk and Pustejovsky. |
Reject |
110_Marek_Veber.pdf |
|
|
|
|
124 |
Diana Zaiu Inkpen |
dianaz@cs.toronto.edu |
2 |
Automatic Sense Disambiguation of the Near-Synonyms in a Dictionary Entry |
Text - word sense disambiguation |
124_Diana_Zaiu_Inkpen_abstract.txt |
Eneko Agirre |
W |
A paper on WSD using a set of well-known unsupervised techniques. The only novelty is to use a small sample of training data to choose the best combination. - section 2: in general it is not always clear when it is the near-synonymy entry, and when the WordNet entry. - the last sentence of the first paragraph in section 2.1 does not add anything. - 2.3: it is not clear what is it that you intersect - 2.6: when talking about the 904 datapoints a reference should be made to section 4 - 3: there is no mention to what is accuracy, and which is the coverage. this point is clarified later in section 6. - 3: I think that it should be stressed that you measure each sense independently. This makes comparison difficult to systems which measure accuracy in the Senseval way. - 3: do all algorithms return always an answer for each sense? - 4: data on the ambiguity on the gold standard (around 450 senses for 282 near-synonyms) - 5: the comparison with the Lesk algorithm is not fair, the simple lesk algorithm gets very low results. - 5: the way to calculate precision and coverage in Senseval is different. refer to their web page. |
Karel Oliva |
A |
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Reject |
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124_Diana_Zaiu_Inkpen.ps |
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127 |
Schwarz Jana |
Jana.Schwarz@mailbox.tu-dresden.de |
2 |
Dialogue Models for Bilingual Human-Computer Interaction in a City Information System |
Dialogue - development of dialogue strategies |
127_Schwarz_Jana_abstract.txt |
Elmar Noeth |
W |
I feel it would be better for most of the readers if the example in chapter 6 is presented translated into English Please provide a reference for the GAT system (chapter 1) also: do you have any experimental evidence for the interesting claim that people expect the system to retain char. of human agents (same paragraph) there is quite a few typos: develope, withal, detailled, exhausting instead of exhaustive templats, Additionaly If you have the chance, have somebody, who is very competent in English, proof-read the final version |
Karel Pala |
A |
-- kp. |
Reject |
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127_Schwarz_Jana.ps |
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128 |
Elena Karagjosova and Ivana Kruijff-Korbayová |
elka@coli.uni-sb.de |
2 |
An Analysis of Conditional Responses in dialogue |
Speech - other |
128_Elena_Karagjosova_abstract.txt |
Elmar Noeth |
A |
Example 2:8 is missing |
Vaclav Matousek |
A |
Interesting contribution, but the problem could be described more comprehensive (why not 8 pages ?). I recommend to extend the papers and then accept for the conference. |
SP |
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128_Elena_Karagjosova.ps |
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130 |
Ghuilaine Clouet |
g.clouet@rd.francetelecom.com |
1 |
Etude de la perception de la qualite des sites Web par les usagers en vue de dimensions d'evaluation et resultats utiles a la conception |
Text - other |
130_Ghuilaine_Clouet_abstract.txt |
Genevieve Baudoin |
W |
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Reject |
130_Ghuilaine_Clouet.pdf |
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131 |
Fernando LLopis Pascul |
llopis@dlsi.ua.es |
1 |
Passage Selection to Improve Question Answering |
Text - information retrieval |
131_Fernando_LLopis_Pascul_abstract.txt |
Karel Pala |
R |
The text of the paper contains grammatical errors (agreement) "...a query term appear(s?) in each document ..." The explanations in the paper seem to be not quite clearly formulated, e.g. the authors refer to ATT IR system but I was not able to decipher if they have in mind their own system and how it may be related to IR n system mentioned in the first sect. Introduction. In sect. 3.1 Porter stemmer is mentioned but there's no reference to it in the Bibliography - thus it is impossible to even guess the reliability some of their results. kp. |
Ivan Kopecek |
A |
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Reject |
131_Fernando_LLopis_Pascul.pdf |
131_Fernando_LLopis_Pascul.ps |
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132 |
Karel Pala |
glum@fi.muni.cz |
2 |
A Procedure for the Semiautomatic Building of Consistent Dictionary Definitions |
Text - other |
132_Karel_Pala_abstract.txt |
Eneko Agirre |
W |
Interesting work. The relation of the goals of the paper and the experiments is not very clear. I think the goals of the paper should be better stated to reflect the experiments. For instance: - is the goal of the automatic analysis of dictionary definitions to enrich WN? - is the goal of the analysis to construct proper definitions for WN? - is it a paper on the relation between human definitions and relations in LKBs? - There is a lack to references in the area of automatic analysis of definitions in dictionaries (Amsler, 81; Vossen, 89; Agirre et. al, 2000 in Euralex for a recent paper with more references). - section 3: I don't think the data confirms that the definitions give always a genus. Some of the most frequent head nouns are who, sort, part. In the examples in the 4th page we can find "a piece of, a set of, a person who". These have been traditionally taken as specific relators in opposition to genus+differentia definitions. - section 3: what is the meaning of 1st file in figure 1. - section 3: is there any measure of evaluation of the results of the syntactic analysis (accuracy, coverage) - Finally some sentences are too long, and somewhat awkward to understand. |
Eduard Hovy |
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Reject |
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132_Karel_Pala.ps |
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133 |
Elena Boian |
lena@math.md |
1 |
The lexical-morphological analysis system of the Roumanian language |
Text - automatic morpholgy |
133_abstract.txt |
Ivan Kopecek |
R |
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Karel Pala |
R |
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Reject |
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133.ps |
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134 |
Agnieszka Mykowiecka |
agn@ipipan.waw.pl |
7 |
A Large-Scale Corpus of Polish and Tools for its Annotation |
Text - text corpora |
134_Agnieszka_Mykowiecka_abstract.txt |
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Demonstration |
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135 |
Antanas Lipeika |
alipeika@dtiltas.lt |
3 |
ISOLATED WORD RECOGNITION AND VISUALIZATION SOFTWARE |
Speech - automatic speech recognition |
135_Antanas_Lipeika_abstract.txt |
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Demonstration |
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136 |
Bronius Tamulynas |
bronius@pit.ktu.lt |
2 |
Computer-based Translation from English to Lithuanian |
Text - machine translation |
136_Bronius_Tamulynas_abstract.txt |
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Demonstration |
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137 |
Alexander Troussov |
ATrousso@ie.ibm.com |
2 |
IBM Dictionary and Linguistic Tools system “Frost” |
Text - other |
137_Alexander_Troussov_abstract.txt |
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Demonstration |
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138 |
Piotr Banski |
bansp@venus.ci.uw.edu.pl |
1 |
XML architecture for a modern corpus |
Text - text corpora |
138_Piotr_Banski_abstract.txt |
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Demonstration |
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139 |
Qian Hu |
qian@mitre.org |
5 |
The MITRE Audio Hot Spotting Prototype - Using Multiple Speech and Natural Language Processing Technologies |
Speech - other |
139_Qian_Hu_abstract.txt |
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Demonstration |
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141 |
Pekar Darko |
pekard@eunet.yu |
3 |
ALFANUM SYSTEM FOR CONTINUOUS SPEECH RECOGNITION |
Speech - automatic speech recognition |
141_Pekar_Darko_abstract.txt |
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Demonstration |
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142 |
Elena Boian |
lena@math.md |
2 |
Romanian words inflection |
Text - automatic morphology |
142_Elena_Boian_abstract.txt |
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Demonstration |
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143 |
Elena Karagjosova |
elka@coli.uni-sb.de, stinae@ling.gu.se, korbay@CoLi.Uni-SB.DE |
3 |
GoDiS - Issue-based dialogue management in a multi-domain, multi-language dialogue system |
Dialogue - dialogue systems |
143_Elena_Karagjosova_abstract.txt |
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Demonstration |
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144 |
Demidova Valentina |
demidova@math.md |
2 |
Hyphenation algorithm for Romanian language words |
Text - parsing and part-of-speech tagging |
144_Demidova_Valentina_abstract.txt |
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Demonstration |
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145 |
Styve Jaumotte |
jaumotte@info.univ-angers.fr |
3 |
Semantic Knowledge in an Information Retrieval System |
Text - information retrieval |
145_Styve_Jaumotte_abstract.txt |
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Demonstration |
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146 |
Petr Schwarz |
schwarzp@fit.vutbr.cz |
4 |
Keyword spotting system |
Speech - automatic speech recognition |
146_Petr_Schwarz_abstract.txt |
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Demonstration |
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201 |
Michael Bodasov |
boldasov@nm.ru |
2 |
Generator module for InBASE NL data base Interface system |
Text - information retrieval |
201_Michael_Bodasov_abstract.txt |
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Demonstration |
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202 |
Beate Dorow |
beate.dorow@ims.uni-stuttgart.de |
3 |
Visualisation Techniques for Analysing Meaning |
Text - knowledge representation and reasoning |
202_Beate_Dorow_abstract.txt |
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Demonstration |
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203 |
Zhiping Zheng |
zheng@coli.uni-sb.de |
1 |
Deploying Web-based Question Answering System to Local Archive |
Text - information retrieval |
203_Zhiping_Zheng_abstract.txt |
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Demonstration |
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204 |
Pavel Rychly |
pary@fi.muni.cz |
1 |
Advance concordances with Bonito |
Text - text corpora |
204_Pavel_Rychly_abstract.txt |
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Demonstration |
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205 |
Jan Šedivý |
jan_sedivy@cz.ibm.com |
1 |
Demonstration of multi-modal applications on IPAQ |
Speech - automatic speech recognition |
205_Jan_Šedivý_abstract.txt |
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Demonstration |
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206 |
pics |
dafdsdf@ewuiooor.net |
pics |
pics |
Text - text corpora |
206_pics_abstract.txt |
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