Word-Sense Classification by Hierarchical Clustering

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.