Lexical feature transfer - First report - Revision history

Francis Tyers at 17:47, 25 July 2012

2012-07-25T17:47:31Z

2012-06-10T14:23:45Z

2012-06-10T14:22:26Z

2012-06-10T14:15:47Z

2012-06-10T14:15:26Z

2012-06-10T14:15:10Z

2012-06-10T14:14:50Z

2012-06-10T14:14:27Z

2012-06-10T14:12:25Z

2012-06-10T14:11:46Z

← Older revision		Revision as of 17:47, 25 July 2012
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	The WER dropped from 7.9% to 5.2% after the model was applied on unseen data.		The WER dropped from 7.9% to 5.2% after the model was applied on unseen data.

			[[Category:Development]]

@@ Line 34: / Line 34: @@
 A linear SVM was trained. Cross validation on the training set showed an accuracy of 91%.
-The PER dropped from 83.39, 82.97%, and the WER from 118.97% to 119.25%. However, the original testing corpus has 49.929 non-blank lines, while the translated one has 49.919, 10 lines less. This is the reason why the errors are so high. Re-evaluation will be done as soon as the two corpora are manually realigned.
+The PER dropped from 83.39, 82.97%, and the WER from 119.25% to 118.97%. However, the original testing corpus has 49.929 non-blank lines, while the translated one has 49.919, 10 lines less. This is the reason why the errors are so high. Re-evaluation will be done as soon as the two corpora are manually realigned.
 This method takes into account only the original (manually written) target side corpus (english in this case).

@@ Line 34: / Line 34: @@
 A linear SVM was trained. Cross validation on the training set showed an accuracy of 91%.
-The PER dropped from 83.39, 82.97%, and the WER from 118.97% to 119.25%. However, the original testing corpus has 49.929 non-blank lines, while the translated one has 49.919, 10 lines less, and this is the reason why the errors are so high. Re-evaluation will be done as soon as the two corpora are manually realigned.
+The PER dropped from 83.39, 82.97%, and the WER from 118.97% to 119.25%. However, the original testing corpus has 49.929 non-blank lines, while the translated one has 49.919, 10 lines less. This is the reason why the errors are so high. Re-evaluation will be done as soon as the two corpora are manually realigned.
 This method takes into account only the original (manually written) target side corpus (english in this case).

@@ Line 11: / Line 11: @@
 <pre>
 cat file | apertium-destxt | lt-proc xx-yy.automorph.bin | apertium-tagger -g xx-yy.prob \
-| apertium-destxt | some-feature-extractor.
+| apertium-destxt | some-pattern-extractor
 </pre>
 -------------------------

@@ Line 10: / Line 10: @@
 A typical pipeline for extracting patterns would look like this: <br />
 <pre>
-cat file | apertium-destxt | lt-proc xx-yy.automorph.bin | apertium-tagger -g xx-yy.prob \ <br>
+cat file | apertium-destxt | lt-proc xx-yy.automorph.bin | apertium-tagger -g xx-yy.prob \
 | apertium-destxt | some-feature-extractor.
 </pre>

@@ Line 9: / Line 9: @@
 A typical pipeline for extracting patterns would look like this: <br />
+<pre>
 cat file | apertium-destxt | lt-proc xx-yy.automorph.bin | apertium-tagger -g xx-yy.prob | apertium-destxt | some-feature-extractor.
+</pre>
 -------------------------
 Patterns in the form of <n1> <pr> <the>? <n2> were extracted in the following way:

@@ Line 4: / Line 4: @@
 Manually written aligner for <n> <pr> <n> patterns (java, c++ version has some bugs). <br />
 Manually written nominal to numerical feature converter, needed for SVM training (java) <br />
+Rapidminer <br />
+liblinear / libsvm <br />
 -------------------------

@@ Line 1: / Line 1: @@
-Tools used:
+Tools used: <br />
-Manually written parser for the output of apertium-tagger. (java, c++).
+Manually written parser for the output of apertium-tagger. (java, c++). <br />
-Manually written pattern extractor (c++).
+Manually written pattern extractor (c++). <br />
-Manually written aligner for <n> <pr> <n> patterns (java, c++ version has some bugs).
+Manually written aligner for <n> <pr> <n> patterns (java, c++ version has some bugs). <br />
-Manually written nominal to numerical feature converter, needed for SVM training (java)
+Manually written nominal to numerical feature converter, needed for SVM training (java) <br />
 -------------------------