Towards the Automatic Identification of Adjectival Scales: Clustering Adjectives According to Meaning

TOWARDS THE AUTOMATIC IDENTIFICATION OF ADJECTIVAL SCALES: CLUSTERING ADJECTIVES ACCORDING TO MEANING Vasileios Hatzivassiloglou Kathleen R. McKeown Department of Computer Science 450 Computer Science Building Columbia University New York, N.Y. 10027 Internet: vh@cs.columbia.edu kathy @ cs.columbia.edu ABSTRACT In this paper we present a method to group adjectives according to their meaning, as a first step towards the automatic identification of adjectival scales. We discuss the properties of adjectival scales and of groups of semantically related adjectives and how they imply sources of linguistic knowledge in text corpora. We describe how our system exploits this linguistic knowledge to compute a measure of similarity between two adjectives, using statistical tech- niques and without having access to any semantic infor- mation about the adjectives. We also show how a clustering algorithm can use these similarities to produce the groups of adjectives, and we present results produced by our sys- tem for a sample set of adjectives. We conclude by present- ing evaluation methods for the task at hand, and analyzing the significance of the results obtained. 1. INTRODUCTION As natural language processing systems become more oriented towards solving real-world problems like machine translation or spoken language under- standing in a limited domain, their need for access to vast amounts of knowledge increases. While a model of the general rules of the language at various levels (morphological, syntactic, etc.) can be hand-encoded, knowledge which pertains to each specific word is harder to encode manually, if only because of the size of the lexicon. Most systems currently rely on human linguists or lexicographers who compile lexicon entries by hand. This approach requires significant amounts of time and effort for expanding the system's lexicon. Furthermore, if the compiled infor- mation depends in any way on the domain of the application, the acquisition of lexical knowledge must be repeated whenever the system is transported to another domain. For systems which need access to large lexicons, some form of at least partial automa- tion of the lexical knowledge acquisition phase is needed. One type of lexical knowledge which is useful for many natural language (NL) tasks is the semantic re- latedness between words of the same or different syn- tactic categories. Semantic relatedness subsumes hyponymy, synonymy, and antonymy- incompatibility. Special forms of relatedness are represented in the lexical entries of the WordNet lex- ical database (Miller et al., 1990). Paradigmatic semantic relations in WordNet have been used for diverse NL problems, including disambiguation of syntactic structure (Resnik, 1993) and semi- automatic construction of a large-scale ontology for machine translation (Knight, 1993). In this paper, we focus on a particular case of semantic relatedness: relatedness between adjectives which describe the same property. We describe a technique for automatically grouping adjectives ac- cording to their meaning based on a given text cor- pus, so that all adjectives placed in one group describe different values of the same property. Our method is based on statistical techniques, augmented with linguistic information derived from the corpus, and is completely domain independent. It demonstrates …

Content truncated to 500 words