Modeling Word Senses With Fuzzy Clustering

This thesis describes a clustering approach to automatically inferring soft semantic classes and characterizing senses of a set of Norwegian nouns. The words are represented by way of their distribution in text, identified as local contexts in the form of lexical-syntactic relations. Through a shallow processing step the context features are extracted for lemmatized word forms in syntactically tagged corpora. The corresponding frequency counts of noun–context co-occurrences are weighted with a statistical association measure, and the distributional profile of a given word is represented in the form of a feature vector in a semantic space model. A hybrid approach is taken when clustering the word vectors; a bottom-up hierarchical method is used to initialize various types of fuzzy partitional clusterings. With the purpose of capturing the notion of typicality the clusters are construed as fuzzy sets, and the words are assigned varying degrees of membership with respect to the various classes. Words are assigned graded memberships in clusters on the basis of their resemblance towards a class prototype. The goal is to automatically uncover semantic classes, where the various memberships of a given word in these fuzzy clusters can be used to characterize its various senses.