Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon. Semantic information can be helpful in almost all aspects of natural language understanding, including word sense disambiguation, selectional restrictions, attachment decisions, and discourse processing. Semantic knowledge can add a great deal of power and accuracy to natural language processing systems. But semantic information is difficult to obtain. In most cases, semantic knowledge is encoded manually for each application. There have been a few large-scale efforts to create broad semantic knowledge bases, such as WordNet (Miller, 1990) and Cyc (Lenat, Prakash, and Shepherd, 1986) . While these efforts may be useful for some applications, we believe that they will never fully satisfy the need for semantic knowledge. Many domains are characterized by their own sublanguage containing terms and jargon specific to the field. Representing all sublanguages in a single knowledge base would be nearly impossible. Furthermore, domain-specific semantic lexicons are useful for minimizing ambiguity problems. Within the context of a restricted domain, many polysemous words have a strong preference for one word sense, so knowing the most probable word sense in a domain can strongly constrain the ambiguity. We have been experimenting with a corpusbased method for building semantic lexicons semiautomatically. Our system uses a text corpus and a small set of seed words for a category to identify other words that also belong to the category. The algorithm uses simple statistics and a bootstrapping mechanism to generate a ranked list of potential category words. A human then reviews the top words and selects the best ones for the dictionary. Our approach is geared toward fast semantic lexicon construction: given a handful of seed words for a category and a representative text corpus, one can build a semantic lexicon for a category in just a few minutes. In the first section, we describe the statistical bootstrapping algorithm for identifying candidate category words and ranking them. Next, we describe experimental results for five categories. Finally, we discuss our experiences with additional categories and seed word lists, and summarize our results.
Early bootstrapping approaches such as REF were based on few seed words of a semantic category and their nearest neighbor contexts.
A Corpus-Based Approach For Building Semantic Lexicons
Conference On Empirical Methods In Natural Language Processing
Computer Science