Difference between revisions of "User:Deadbeef/LexicalSelection"

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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
 
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
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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
 
   
   

Latest revision as of 20:07, 6 July 2009

Introduction[edit]

Feel free to edit/comment/spam/anything here


Some formalizing[edit]

IMHO the LS problem can be reduced to a classification problem:

the context could be a text frame, a bag of words, a tfidf-labelled array etc.

the possible translations for w can be obtained maybe from WordNet? or another dictionary?

We already have a set of attributes (srl and slr) to mark ambiguous words; it would be best to use those. en-ca and en-es have examples -- Jimregan 13:22, 21 June 2009 (UTC)


Awesome :D I'll give it a read in the next days :) Thanks a lot! -- 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[1] - 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.


Data Mining/Machine Learning tools supporting the classification task[edit]

I've tried many tools while taking AI and DM-related classes, like Weka[2] (that I've integrated in a Multi-Agent System to support agents while taking decisions) or RapidMiner[3], but I think the most appropriate tool to use in this case could be Orange[4]. Now I'm doing some experiments in using its APIs from C++ and Python.


Some Bookmarks (please feel free to add more)[edit]

Using UMLS Concept Unique Identifiers (CUIs) for Word Sense Disambiguation in the Biomedical Domain: http://www.d.umn.edu/~tpederse/Pubs/amia07.pdf

Word Sense Disambiguation - Algorithms and Applications: http://www.wsdbook.org/

Word Sense Disambiguation: The State of the Art: http://sites.univ-provence.fr/~veronis/pdf/1998wsd.pdf

Word Sense Disambiguation (slide from the "Linguaggi e Traduttori" class): http://www.di.uniba.it/~semeraro/LT/WSD.pdf

Perl scripts doing WSD and mapping on UMLS ontologies: http://cuitools.sourceforge.net/

Nice ACM survey on WSD: http://www.dsi.uniroma1.it/~navigli/pubs/ACM_Survey_2009_Navigli.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

http://www.dlsi.ua.es/~mlf/docum/sanchezvillamil04p.pdf

http://www.dlsi.ua.es/~mlf/docum/sanchezvillamil05p.pdf