Finalni upravy

[pan13-paper.git] / pan13-paper / yenya-text_alignment.tex

diff --git a/pan13-paper/yenya-text_alignment.tex b/pan13-paper/yenya-text_alignment.tex
index 1cf67e7b18eebf354293184af9bf3ca49e8bd51c..1b54ac7bc478f5558345f4fea44e697e2bed93e6 100755 (executable)
--- a/pan13-paper/yenya-text_alignment.tex
+++ b/pan13-paper/yenya-text_alignment.tex
@@ -34,8 +34,8 @@ In the next sections, we summarize the modifications we did for PAN 2013.
 \subsection{Alternative Features}\r
 \label{altfeatures}\r
 \r
 \subsection{Alternative Features}\r
 \label{altfeatures}\r
 \r

-In PAN 2012, we have used word 5-grams and stop-word 8-grams.\r
-This year we have experimented with different word $n$-grams, and also\r
+In PAN 2012, we used word 5-grams and stop-word 8-grams.\r
+This year we experimented with different word $n$-grams, and also\r

 with contextual $n$-grams as described in \cite{torrejondetailed}.\r
 Modifying the algorithm to use contextual $n$-grams created as word\r
 5-grams with the middle word removed (i.e. two words before and two words\r
 with contextual $n$-grams as described in \cite{torrejondetailed}.\r
 Modifying the algorithm to use contextual $n$-grams created as word\r
 5-grams with the middle word removed (i.e. two words before and two words\r


@@ -43,7 +43,7 @@ after the context) yielded better score:
 \r
 \plagdet{0.7421}{0.6721}{0.8282}{1.0000}\r
 \r
 \r
 \plagdet{0.7421}{0.6721}{0.8282}{1.0000}\r
 \r

-We have then made tests with plain word 4-grams, and to our surprise,\r
+We then made tests with plain word 4-grams, and to our surprise,\r

 it gave even better score than contextual $n$-grams:\r
 \r
 \plagdet{0.7447}{0.7556}{0.7340}{1.0000}\r
 it gave even better score than contextual $n$-grams:\r
 \r
 \plagdet{0.7447}{0.7556}{0.7340}{1.0000}\r


@@ -55,7 +55,7 @@ training corpus parts, plain word 4-grams were better at all parts
 of the corpus (in terms of plagdet score), except the 02-no-obfuscation\r
 part.\r
 \r
 of the corpus (in terms of plagdet score), except the 02-no-obfuscation\r
 part.\r
 \r

-In our final submission, we have used word 4-grams and stop-word 8-grams.\r
+In our final submission, we used word 4-grams and stop-word 8-grams.\r

 \r
 \subsection{Global Postprocessing}\r
 \r
 \r
 \subsection{Global Postprocessing}\r
 \r


@@ -70,17 +70,17 @@ optimizations and postprocessing, similar to what we did for PAN 2010.
 %for development, where it has provided a significant performance boost.\r
 %The official performance numbers are from single-threaded run, though.\r
 \r
 %for development, where it has provided a significant performance boost.\r
 %The official performance numbers are from single-threaded run, though.\r
 \r

-For PAN 2010, we have used the following postprocessing heuristics:\r
+For PAN 2010, we used the following postprocessing heuristics:\r

 If there are overlapping detections inside a suspicious document,\r
 keep the longer one, provided that it is long enough. For overlapping\r
 If there are overlapping detections inside a suspicious document,\r
 keep the longer one, provided that it is long enough. For overlapping\r

-detections up to 600 characters, drop them both. We have implemented\r
-this heuristics, but have found that it led to a lower score than\r
+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
 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
+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
 so it is favourable to keep the detection even though the evidence\r
 for it is rather low.\r
 \r


@@ -90,7 +90,7 @@ The global postprocessing improved the score even more:
 \r
 \subsection{Evaluation Results and Future Work}\r
 \r
 \r
 \subsection{Evaluation Results and Future Work}\r
 \r

-       The evaulation on the competition corpus had the following results:\r
+       The evaluation on the competition corpus had the following results:\r

 \r
 \plagdet{0.7448}{0.7659}{0.7251}{1.0003}\r
 \r
 \r
 \plagdet{0.7448}{0.7659}{0.7251}{1.0003}\r
 \r


@@ -113,9 +113,9 @@ of Graduate Theses,\\ \url{http://theses.cz}}.
 \r
 We plan to experiment further with combining more than two types\r
 of features, be it continuous $n$-grams or contextual features.\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 aggresive heuristics for joining\r
+This should allow us to tune down the aggressive heuristics for joining\r

 neighbouring detections, which should lead to higher precision,\r
 neighbouring detections, which should lead to higher precision,\r

-hopefully without sacrifying recall.\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
 \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