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.

1. Error analysis ofWord
Sense Disambiguation
Ruben Izquierdo
Marten Postma
PiekVossen
Izquierdo,PostmaandVossen
VUAmsterdam

2. Motivation
 Word Sense Disambiguation is still an unsolved problem
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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)
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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
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5. Monosemous errors
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6. Monosemous errors
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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. Most Frequent Sense
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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
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9. Analysis per PoS-tag
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10. Analysis per polysemy class
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2Senses
Poly. C.
6 15
Low Medium High

11. Analysis per frequency class
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12. Most difficult words
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13. Expected vs. Observed
difficulties
 Calculate per sentence
 The “expected” difficulty
 Average polysemy, sentence length, average word length
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14.  Calculate per sentence
 The “expected” difficulty
 Average polysemy, sentence length, average word length
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Expected vs. Observed
difficulties

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
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Expected vs. Observed
difficulties

16. 16 Izquierdo, Postma and Vossen VU Amsterdam
Expected vs. Observed
difficulties

17. 17 Izquierdo, Postma and Vossen VU Amsterdam
Expected vs. Observed
difficulties

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
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Expected vs. Observed
difficulties

19. WSD Corpora
http://github.com/rubenIzquierdo/wsd_corpora
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20. WSD Corpora
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21. System Outputs
https://github.com/rubenIzquierdo/sval_systems
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22. System Outputs
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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
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24. Analysis per PoS-tag
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