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
-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
@@ -43,7 +43,7 @@ after the context) yielded better score:
 \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
@@ -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
-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
@@ -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 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
-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
-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
@@ -90,7 +90,7 @@ The global postprocessing improved the score even more:
 \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
@@ -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
-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
-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