User:Deadbeef/LexicalSelection - Revision history

Deadbeef at 20:07, 6 July 2009

2009-07-06T20:07:40Z

2009-07-06T20:06:30Z

2009-07-02T15:30:16Z

2009-07-02T15:26:27Z

2009-06-30T21:49:31Z

2009-06-21T13:22:00Z

srl and slr

2009-06-20T15:24:33Z

2009-06-20T14:24:14Z

Some formalizing

2009-06-20T14:23:58Z

Some formalizing

2009-06-20T14:16:34Z

@@ Line 23: / Line 23: @@
 It seems that the WSD problem can be handled with a Inductive Logic Programming-oriented approach, as this paper states: http://www.mt-archive.info/ACL-2007-Specia.pdf
-I'm currently trying to introduce probabilistic reasoning into Aleph - the Inductive Logic Programming framework cited in the paper - for a university project, it would be interesting to see how it performs with lexical selection :) I'll commit some code when I get it to work :D
+I'm currently trying to introduce probabilistic reasoning into Aleph[http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html] - the Inductive Logic Programming framework cited in the paper - for a university project and maybe it would be interesting to see how it could handle with lexical selection.
-Aleph can be found here: http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html - BEWARE: it's written in Prolog

@@ Line 26: / Line 26: @@
 Aleph can be found here: http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html - BEWARE: it's written in Prolog
+= Data Mining/Machine Learning tools supporting the classification task =
+I've tried many tools while taking AI and DM-related classes, like Weka[http://www.cs.waikato.ac.nz/ml/weka/] (that I've integrated in a Multi-Agent System to support agents while taking decisions) or RapidMiner[http://www.rapidminer.com], but I think the most appropriate tool to use in this case could be Orange[http://www.ailab.si/Orange/]. Now I'm doing some experiments in using its APIs from C++ and Python.
 = Some Bookmarks (please feel free to add more) =

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 Parameter reduction in unsupervisedly trained sliding-window part-of-speech taggers: http://transducens.dlsi.ua.es/repositori/transducens/pubs/167/ranlp05.pdf
+http://www.dlsi.ua.es/~mlf/docum/sanchezvillamil04p.pdf
+http://www.dlsi.ua.es/~mlf/docum/sanchezvillamil05p.pdf

← Older revision		Revision as of 15:26, 2 July 2009
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	Verb Semantics and Lexical Selection: http://www.ldc.upenn.edu/acl/P/P94/P94-1019.pdf		Verb Semantics and Lexical Selection: http://www.ldc.upenn.edu/acl/P/P94/P94-1019.pdf

			Parameter reduction in unsupervisedly trained sliding-window part-of-speech taggers: http://transducens.dlsi.ua.es/repositori/transducens/pubs/167/ranlp05.pdf

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 the context <math>c</math> could be a text frame, a bag of words, a tfidf-labelled array etc.
-the possible translations for w can be obtained maybe for WordNet? or another dictionary?
+the possible translations for w can be obtained maybe from WordNet? or another dictionary?
 {{comment|We already have a set of attributes (<code>srl</code> and <code>slr</code>) to mark ambiguous words; it would be best to use those. en-ca and en-es have examples -- [[User:Jimregan|Jimregan]] 13:22, 21 June 2009 (UTC)}}
+{{comment|Awesome :D I'll give it a read in the next days :) Thanks a lot! -- [[User:Deadbeef|Deadbeef]] 23:53, 30 June 2009 (UTC)}}
 the classification problem can be solved in various ways: support vector machines, naive-bayes classifier, decision tree etc.
+It seems that the WSD problem can be handled with a Inductive Logic Programming-oriented approach, as this paper states: http://www.mt-archive.info/ACL-2007-Specia.pdf
+I'm currently trying to introduce probabilistic reasoning into Aleph - the Inductive Logic Programming framework cited in the paper - for a university project, it would be interesting to see how it performs with lexical selection :) I'll commit some code when I get it to work :D
+Aleph can be found here: http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html - BEWARE: it's written in Prolog
 = Some Bookmarks (please feel free to add more) =

@@ Line 8: / Line 8: @@
 IMHO the LS problem can be reduced to a classification problem:
-:<math>\mathrm{classify}(word\ w,\ context\ c)\ \in\ \{\ m\ :\ m\ meaning\ associated\ to\ w\ \}.</math>
+:<math>\mathrm{classify}(word\ w,\ context\ c)\ \in\ \{\ t\ :\ t\ possible\ translation\ for\ w\ \}.</math>
 the context <math>c</math> could be a text frame, a bag of words, a tfidf-labelled array etc.
-the meanings associated to a word can be obtained for example from WordNet
+the possible translations for w can be obtained maybe for WordNet? or another dictionary?
 the classification problem can be solved in various ways: support vector machines, naive-bayes classifier, decision tree etc.

@@ Line 1: / Line 1: @@
 = Introduction =
+Feel free to edit/comment/spam/anything here
-Hello world!