Using LSA and Noun Coordination Information to Improve the Recall and Precision of Automatic Hyponymy Extraction

Using LSA and Noun Coordination Information to Improve the Precision and Recall of Automatic Hyponymy Extraction Scott Cederberg Dominic Widdows Center for the Study of Language and Information 210 Panama Street Stanford University Stanford CA 94305 {cederber,dwiddows}@csli.stanford.edu Abstract In this paper we demonstrate methods of im- proving both the recall and the precision of au- tomatic methods for extraction of hyponymy (IS A) relations from free text. By applying la- tent semantic analysis (LSA) to filter extracted hyponymy relations we reduce the rate of er- ror of our initial pattern-based hyponymy ex- traction by 30%, achieving precision of 58%. Applying a graph-based model of noun-noun similarity learned automatically from coordi- nation patterns to previously extracted correct hyponymy relations, we achieve roughly a five- fold increase in the number of correct hy- ponymy relations extracted. 1 Introduction This paper demonstrates that mathematical models for measuring semantic similarity between concepts can be used to improve the learning of hyponymy relationships between concepts from free text. In particular, we show that latent semantic analysis can be used to filter results, giving an increase in precision, and that neighbors in a graph built from coordination information can be used to improve recall. The goal of extracting semantic information from text is well-established, and has encouraged work on lexical acquisition (Roark and Charniak, 1998), information ex- traction (Cardie, 1997), and ontology engineering (Hahn and Schnattinger, 1998). The purpose of this kind of work is to collect information about the meanings of lexi- cal items or phrases, and the relationships between them, so that the process of building semantic resources (such as ontologies and dictionaries) by hand can be automated or at least helped. One of the standard ways of arranging concepts is in a concept hierarchy or taxonomy such as the WordNet noun taxonomy (Fellbaum, 1998). The fundamental relation- ship between objects in a taxonomy is called hyponymy, where y is a hyponym of x if every y is also an x. For example, every trout is also a fish, so we say that trout is a hyponym (“below name”) of fish and conversely, fish is a hypernym (“above name”) of trout. Other names ex- ist for variants of the hyponymy relationship, such as an IS A relationship, a parent-node / child-node relationship, and a broader term / narrower term relationship. It is also noted that the genus of an object, in traditional lexico- graphic terms, is often a hypernym of that object (Guthrie et al., 1996). Throughout this paper we will write y < x for the relationship “y is a hyponym of x”. In this paper, we use the hyponymy relationship to describe subset re- lationships, so we regard y < x to be true if the set of y’s can reasonably be said to be a subset of the set of x’s.1 Because hyponymy relationships are so central to knowledge engineering, there have been numerous at- tempts to learn them from text, beginning with those of Hearst (1992). We review this work …

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