Abstract
This paper investigates the use of clustering techniques
in word-sense classification, which identifies different contexts that a word
was used with the same or similar sense. For simplicity, we have used the
hierarchical clustering techniques: single- and complete-linkage, and we showed
that the latter is a more suitable technique from our performance measurements
(i.e. recall and precision) compared with manually grouping different contexts
of similar meaning. We found that the use of part-of-speech tags and
fixed-length context has better clustering performance than without
part-of-speech tags and sentence context, respectively. The differences between
manually identified groups of different contexts are measured in terms of recall
and precision at about 80%, which are not very different from the average recall
and precision performance of complete-linkage clustering at 80% and 75%,
respectively.
Keyword:Clustering, Word Sense Disambiguation, Word Sense Classification, Lexicography, Corpus Analysis, Unsupervised Learning
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