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Error analysis of Word Sense DisambiguationRuben IzquierdoMarten PostmaPiek Vossen
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Motivation
Word Sense Disambiguation is still an unsolved problem
2 Izquierdo, Postma and Vossen VU Amsterdam
Error Analysis
Perform error analysis on previous WSD 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-words WSD and entity linking (#12)
3 Izquierdo, Postma and Vossen VU Amsterdam
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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Monosemous errors
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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.ncordgrass.n
Semeval2013 (lemmas)
348 (21.1%) 1.9% Private_enterprise, developing_country, narrow_margin
Most Frequent Sense
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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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Analysis per PoS-tag
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Analysis per polysemy class
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2Senses
Poly. C.
6 15
Low Medium High
Analysis per frequency class
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Most difficult words
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Expected vs. Observeddifficulties
Calculate per sentence
The “expected” difficulty
Average polysemy, sentence length, average word length
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Calculate per sentence
The “expected” difficulty
Average polysemy, sentence length, average word length
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Expected vs. Observeddifficulties
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. Observeddifficulties
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Expected vs. Observeddifficulties
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Expected vs. Observeddifficulties
• 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 appears18 Izquierdo, Postma and Vossen VU Amsterdam
Expected vs. Observeddifficulties
WSD Corpora
http://github.com/rubenIzquierdo/wsd_corpora
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WSD Corpora
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System Outputs
https://github.com/rubenIzquierdo/sval_systems
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System Outputs
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Error analysis of Word Sense Disambiguation
Ruben Izquierdo
Marten Postma
Piek Vossen
http://github.com/rubenIzquierdo/wsd_corpora
http://github.com/rubenIzquierdo/sval_systems
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Analysis per PoS-tag
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