+detections up to 600 characters, drop them both. We implemented\r
+this heuristics, but found that it led to a lower score than\r
+without this modification. Further experiments with global postprocessing\r
+of overlaps led to a new heuristics: we unconditionally drop overlapping\r
+detections with up to 250 characters both, but if at least one of them\r
+is longer, we keep both detections. This is probably a result of\r
+plagdet being skewed too much towards recall (because the percentage of\r
+plagiarized cases in the corpus is way too high compared to real-world),\r
+so it is favourable to keep the detection even though the evidence\r
+for it is rather low.\r
+\r
+The global postprocessing improved the score even more:\r
+\r
+\plagdet{0.7469}{0.7558}{0.7382}{1.0000}\r
+\r
+\subsection{Evaluation Results and Future Work}\r
+\r
+ The evaluation on the competition corpus had the following results:\r
+\r
+\plagdet{0.7448}{0.7659}{0.7251}{1.0003}\r
+\r
+This is quite similar to what we have seen on a training corpus,\r
+with only the granularity different from 1.000 being a bit surprising.\r
+%, given\r
+%the aggressive joining of neighbouring detections we perform.\r
+Compared to the other participants, our algorithm performs\r
+especially well for human-created plagiarism (the 05-summary-obfuscation\r
+sub-corpus), which is where we want to focus for our production\r
+systems\footnote{Our production systems include the Czech National Archive\r
+of Graduate Theses,\\ \url{http://theses.cz}}.\r
+\r
+% After the final evaluation, we did further experiments\r
+%with feature types, and discovered that using stop-word 8-grams,\r
+%word 4-grams, {\it and} contextual $n$-grams as described in\r
+%Section \ref{altfeatures} performs even better (on a training corpus):\r
+%\r
+%\plagdet{0.7522}{0.7897}{0.7181}{1.0000}\r
+\r
+We plan to experiment further with combining more than two types\r
+of features, be it continuous $n$-grams or contextual features.\r
+This should allow us to tune down the aggressive heuristics for joining\r
+neighbouring detections, which should lead to higher precision,\r
+hopefully without sacrificing recall.\r
+\r
+ As for the computational performance, it should be noted that\r
+our software is prototyped in a scripting language (Perl), so it is not\r
+the fastest possible implementation of the algorithm used. The code\r
+contains about 800 non-comment lines of code, including the parallelization\r
+of most parts and debugging/logging statements.\r
+\r
+ The system is mostly language independent. The only language dependent\r
+part of the code is the list of English stop-words for stop-word $n$-grams.\r
+We use no stemming or other kinds of language-dependent processing.\r