Automatic construction of a hypernym-labeled noun hierarchy from text

Automatic construction of a hypernym-labeled noun hierarchy from text Sharon A. Caraballo Dept. of Computer Science Brown University Providence, RI 02912 sc@cs, brown, edu Abstract Previous work has shown that automatic methods can be used in building semantic lexicons. This work goes a step further by automatically creating not just clusters of related words, but a hierarchy of nouns and their hypernyms, akin to the hand-built hi- erarchy in WordNet. 1 Introduction The purpose of this work is to build some- thing like the hypernym-labeled noun hierar- chy of WordNet (Fellbaum, 1998) automat- ically from text using no other lexical re- sources. WordNet has been an important re- search tool, but it is insufficient for domain- specific text, such as that encountered in the MUCs (Message Understanding Confer- ences). Our work develops a labeled hierar- chy based on a text corpus. In this project, nouns are clustered into a hierarchy using data on conjunctions and ap- positives appearing in the Wall Street Jour- nal. The internal nodes of the resulting tree are then labeled with hypernyms for the nouns clustered underneath them, also based on data extracted from the Wall Street Jour- nal. The resulting hierarchy is evaluated by human judges, and future research directions are discussed. 2 Building the noun hierarchy The first stage in constructing our hierar- chy is to build an unlabeled hierarchy of nouns using bottom-up clustering methods (see, e.g., Brown et al. (1992)). Nouns are clustered based on conjunction and apposi- tive data collected from the Wall Street Jour- nal corpus. Some of the data comes from the parsed files 2-21 of the Wall Street Journal Penn Treebank corpus (Marcus et al., 1993), and additional parsed text was obtained by parsing the 1987 Wall Street Journal text us- ing the parser described in Charniak et al. (1998). From this parsed text, we identified all conjunctions of noun phrases (e.g., "execu- tive vice-president and treasurer" or "scien- tific equipment, apparatus and disposables") and all appositives (e.g., "James H. Rosen- field, a former CBS Inc. executive" or "Boe- ing, a defense contractor"). The idea here is that nouns in conjunctions or appositives tend to be semantically related, as discussed in Riloff and Shepherd (1997) and Roark and Charniak (1998). Taking the head words of each NP and stemming them results in data for about 50,000 distinct nouns. A vector is created for each noun contain- ing counts for how many times each other noun appears in a conjunction or appositive with it. We can then measure the similarity of the vectors for two nouns by computing the cosine of the angle between these vec- tors, as V*W cos (v, w) - Ivi Iwi To compare the similarity of two groups of nouns, we define similarity as the average of the cosines between each pair of nouns made up of one noun from each of the two groups. sim(A,B) = Ev,wCOS (v,w) size(A)size(B) where v ranges over all vectors for nouns 120 in group A, w ranges over …

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