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Error analysis of Word Sense Disambiguation

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CLIN-2015 Presentation
Word Sense Disambiguation is still an unsolved problem in Natural Language Processing.
We claim that most approaches do not model the context correctly, by relying
too much on the local context (the words surrounding the word in question), or on
the most frequent sense of a word. In order to provide evidence for this claim, we
conducted an in-depth analysis of all-words tasks of the competitions that have been
organized (Senseval 2&3, Semeval-2007, Semeval-2010, Semeval 2013). We focused
on the average error rate per competition and across competitions per part of speech,
lemma, relative frequency class, and polysemy class. In addition, we inspected the
“difficulty” of a token(word) by calculating the average polysemy of the words in the
sentence of a token. Finally, we inspected to what extent systems always chose the
most frequent sense. The results from Senseval 2, which are representative of other
competitions, showed that the average error rate for monosemous words was 33.3%
due to part of speech errors. This number was 71% for multiword and phrasal verbs.
In addition, we observe that higher polysemy yields a higher error rate. Moreover, we
do not observe a drop in the error rate if there are multiple occurrences of the same
lemma, which might indicate that systems rely mostly on the sentence itself. Finally,
out of the 799 tokens for which the correct sense was not the most frequent sense, system
still assigned the most frequent sense in 84% of the cases. For future work, we plan
to develop a strategy in order to determine in which context the predominant sense
should be assigned, and more importantly when it should not be assigned. One of the
most important parts of this strategy would be to not only determine the meaning of
a specific word, but to also know it’s referential meaning. For example, in the case of
the lemma ‘winner’, we do not only want to know what ‘winner’ means, but we also
want to know what this ‘winner’ won and who this ‘winner’ was.

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Error analysis of Word Sense Disambiguation

  1. 1. Error analysis ofWord Sense Disambiguation Ruben Izquierdo Marten Postma PiekVossen Izquierdo,PostmaandVossen VUAmsterdam
  2. 2. Motivation  Word Sense Disambiguation is still an unsolved problem 2 Izquierdo, Postma and Vossen VU Amsterdam
  3. 3. Error Analysis  Perform error analysis on previousWSD evaluations to prove our hypothesis  Senseval-2: all-words task  Senseval-3: all-words task  Semeval2007: all-words task (#17)  Semeval2010: all-words on specific domain (#17)  Semeval2013: multilingual all-wordsWSD and entity linking (#12) 3 Izquierdo, Postma and Vossen VU Amsterdam
  4. 4. Motivation  Some “propagated” errors  Errors on monosemous  Errors because pos-tags  Multiwords and phrasal verbs  Little attention has been paid to the real problem  WSD is not 1 problem but N problems  Our hypothesis  Context is not modeled properly in general  System rely too much on the most frequent sense 4 Izquierdo, Postma and Vossen VU Amsterdam
  5. 5. Monosemous errors 5 Izquierdo, Postma and Vossen VU Amsterdam
  6. 6. Monosemous errors 6 Izquierdo, Postma and Vossen VU Amsterdam Competition Monosemous Wrong Examples Senseval2 499 (20.9%) 37.5% gene.n (suppressor_gene.n), chance.a (chance.n) next.r (next.a) Senseval3 334 (16.6%) 44.1% Datum.n (data.n) making.n (make.v) out_of_sight (sight) Semeval2007 25 (5.5%) 11.1% get_stuck.v, lack.v, write_about.v Semeval2010 31 (2.2%) 97.9% Tidal_zone.n pine_marten.n roe_deer.n cordgrass.n Semeval2013 (lemmas) 348 (21.1%) 1.9% Private_enterprise, developing_country, narrow_margin
  7. 7. Most Frequent Sense 7 Izquierdo, Postma and Vossen VU Amsterdam
  8. 8. Most Frequent Sense  When the correct sense is NOT the most frequent sense  Systems still assign mostly the MFS  Senseval2  799 tokens are not MFS  84% systems still assign the MFS  Most “failed” words due to MFS bias  Senseval2, senseval3  Say.v find.v take.v have.v cell.n church.n  Semeval2010  Area.n nature.n connection.n water.n population.n 8 Izquierdo, Postma and Vossen VU Amsterdam
  9. 9. Analysis per PoS-tag 9 Izquierdo, Postma and Vossen VU Amsterdam
  10. 10. Analysis per polysemy class 10 Izquierdo, Postma and Vossen VU Amsterdam 2Senses Poly. C. 6 15 Low Medium High
  11. 11. Analysis per frequency class 11 Izquierdo, Postma and Vossen VU Amsterdam
  12. 12. Most difficult words 12 Izquierdo, Postma and Vossen VU Amsterdam
  13. 13. Expected vs. Observed difficulties  Calculate per sentence  The “expected” difficulty  Average polysemy, sentence length, average word length 13 Izquierdo, Postma and Vossen VU Amsterdam
  14. 14.  Calculate per sentence  The “expected” difficulty  Average polysemy, sentence length, average word length 14 Izquierdo, Postma and Vossen VU Amsterdam Expected vs. Observed difficulties
  15. 15.  Calculate per sentence  The “expected” difficulty  Average polysemy, sentence length, average wor length  The “observed” difficulty  From the real participant outputs, average error rate  We should expect: harder sentences  higher error rate easier sentences   lower error rate 15 Izquierdo, Postma and Vossen VU Amsterdam Expected vs. Observed difficulties
  16. 16. 16 Izquierdo, Postma and Vossen VU Amsterdam Expected vs. Observed difficulties
  17. 17. 17 Izquierdo, Postma and Vossen VU Amsterdam Expected vs. Observed difficulties
  18. 18. • The context is not (probably) exploited properly • Expected “easy” sentences SHOULD show low error rates • Occurrences of the same word in different contexts have similar error rate • The difficulty of a word depends more on its polysemy than on the context where it appears 18 Izquierdo, Postma and Vossen VU Amsterdam Expected vs. Observed difficulties
  19. 19. WSD Corpora http://github.com/rubenIzquierdo/wsd_corpora 19 Izquierdo, Postma and Vossen VU Amsterdam
  20. 20. WSD Corpora 20 Izquierdo, Postma and Vossen VU Amsterdam
  21. 21. System Outputs https://github.com/rubenIzquierdo/sval_systems 21 Izquierdo, Postma and Vossen VU Amsterdam
  22. 22. System Outputs 22 Izquierdo, Postma and Vossen VU Amsterdam
  23. 23. Error analysis of Word Sense Disambiguation Ruben Izquierdo Marten Postma PiekVossen ruben.izquierdobevia@vu.nl http://github.com/rubenIzquierdo/wsd_corpora http://github.com/rubenIzquierdo/sval_systems 23
  24. 24. Analysis per PoS-tag 24 Izquierdo, Postma and Vossen VU Amsterdam

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