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.