A soft-clustering algorithm for automatic induction of semantic classes
Elias Iosif, Alexandros Potamianos · 2007
In this paper, we propose a soft-decision, unsupervised clus-tering algorithm that generates semantic classes automatically using the probability of class membership for each word, rather than deterministically assigning a word to a semantic class. Se-mantic classes are induced using an unsupervised, automatic procedure that uses a context-based similarity distance to mea-sure semantic similarity between words. The proposed soft-decision algorithm is compared with various “hard ” clustering algorithms, e.g., [1], and it is shown to improve semantic class induction performance in terms of both precision and recall for a travel reservation corpus. It is also shown that additional perfor-mance improvement is achieved by combining (auto-induced) semantic with lexical information to derive the semantic simi-larity distance. Index Terms: semantic classes, unsupervised clustering 1.