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Báo cáo khoa học: "Inducing Ontological Co-occurrence Vectors"

In this paper, we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show 73.9% attachment accuracy in the first position and 81.3% accuracy in the top-5 positions. This framework could potentially serve as a foundation for ontologizing lexical-semantic resources and assist the development of other largescale and internally consistent collections of semantic information. . | Inducing Ontological Co-occurrence Vectors Patrick Pantel Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 pantel@isi.edu Abstract In this paper we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show 73.9 attachment accuracy in the first position and 81.3 accuracy in the top-5 positions. This framework could potentially serve as a foundation for on-tologizing lexical-semantic resources and assist the development of other large-scale and internally consistent collections of semantic information. 1 Introduction Despite considerable effort there is still today no commonly accepted semantic corpus semantic framework notation or even agreement on precisely which aspects of semantics are most useful if at all . We believe that one important reason for this rather startling fact is the absence of truly wide-coverage semantic resources. Recognizing this some recent work on wide coverage term banks like WordNet Miller 1990 and CYC Lenat 1995 and annotated corpora like FrameNet Baker et al. 1998 Propbank Kingsbury et al. 2002 and Nombank Meyers et al. 2004 seeks to address the problem. But manual efforts such as these suffer from two drawbacks they are difficult to tailor to new domains and they have internal inconsistencies that can make automating the acquisition process difficult. In this work we introduce a general framework for inducing co-occurrence feature vectors for nodes in a WordNet-like ontology. We believe that this framework will be useful for a variety of applications including adding additional semantic information to existing semantic term banks by disambiguating lexical-semantic resources. Ontologizing semantic resources Recently researchers have applied text- and web-mining algorithms for automatically creating .