Finding Parts in Very Large Corpora

Finding Parts in Very Large Corpora Matthew Berland, Eugene Charniak rob, ec @ cs. brown, edu Department of Computer Science Brown University, Box 1910 Providence, RI 02912 Abstract We present a method for extracting parts of objects from wholes (e.g. "speedometer" from "car"). Given a very large corpus our method finds part words with 55% accuracy for the top 50 words as ranked by the system. The part list could be scanned by an end-user and added to an existing ontology (such as WordNet), or used as a part of a rough semantic lexicon. 1 Introduction We present a method of extracting parts of objects from wholes (e.g. "speedometer" from "car"). To be more precise, given a single word denoting some entity that has recognizable parts, the system finds and rank-orders other words that may denote parts of the entity in question. Thus the relation found is strictly speaking between words, a relation Miller [1] calls "meronymy." In this paper we use the more colloquial "part-of" terminology. We produce words with 55°£ accuracy for the top 50 words ranked by the system, given a very large corpus. Lacking an objective definition of the part-of relation, we use the majority judgment of five human subjects to decide which proposed parts are correct. The program's output could be scanned by an end- user and added to an existing ontology (e.g., Word- Net), or used as a part of a rough semantic lexicon. To the best of our knowledge, there is no published work on automatically finding parts from unlabeled corpora. Casting our nets wider, the work most sim- ilar to what we present here is that by Hearst [2] on acquisition of hyponyms ("isa" relations). In that pa- per Hearst (a) finds lexical correlates to the hyponym relations by looking in text for cases where known hy- ponyms appear in proximity (e.g., in the construction (NP, NP and (NP other NN)) as in "boats, cars, and other vehicles"), (b) tests the proposed patterns for validity, and (c) uses them to extract relations from a corpus. In this paper we apply much the same methodology to the part-of relation. Indeed, in [2] Hearst states that she tried to apply this strategy to the part-of relation, but failed. We comment later on the differences in our approach that we believe were most important to our comparative success. Looking more widely still, there is an ever- growing literature on the use of statistical/corpus- based techniques in the automatic acquisition of lexical-semantic knowledge ([3-8]). We take it as ax- iomatic that such knowledge is tremendously useful in a wide variety of tasks, from lower-level tasks like noun-phrase reference, and parsing to user-level tasks such as web searches, question answering, and digest- ing. Certainly the large number of projects that use WordNet [1] would support this contention. And al- though WordNet is hand-built, there is general agree- ment that corpus-based methods have an advantage in the relative completeness of their coverage, partic- ularly when used as supplements …

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