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Báo cáo khoa học: "Inducing Frame Semantic Verb Classes from WordNet and LDOCE"

This paper presents SemFrame, a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many languagebased applications. When compared to the handcrafted FrameNet, SemFrame achieves its best recall-precision balance with 83.2% recall (based on SemFrame's coverage of FrameNet frames) and 73.8% precision (based on SemFrame verbs’ semantic relatedness to frame-evoking verbs). The next best performing semantic verb classes achieve 56.9% recall and 55.0% precision. . | Inducing Frame Semantic Verb Classes from WordNet and LDOCE Rebecca Green 1 Bonnie J. Dorr and Philip Resnik Institute for Advanced Computer Studies Department of Computer Science College of Information Studies University of Maryland College Park MD 20742 USA rgreen bonnie resnik @umiacs.umd.edu Abstract This paper presents SemFrame a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many languagebased applications. When compared to the handcrafted FrameNet SemFrame achieves its best recall-precision balance with 83.2 recall based on SemFrame s coverage of FrameNet frames and 73.8 precision based on SemFrame verbs semantic relatedness to frame-evoking verbs . The next best performing semantic verb classes achieve 56.9 recall and 55.0 precision. 1 Introduction Semantic content can almost always be expressed in a variety of ways. Lexical synonymy She esteemed him highly vs. She respected him greatly syntactic variation John paid the bill vs. The bill was paid by John overlapping meanings Anna turned at Elm vs. Anna rounded the corner at Elm and other phenomena interact to produce a broad range of choices for most language generation tasks Hirst 2003 Rinaldi et al. 2003 Kozlowski et al. 2003 . At the same time natural language understanding must recognize what remains constant across paraphrases. The paraphrase phenomenon affects many computational linguistic applications including information retrieval information extraction question-answering and machine translation. For example documents that express the same content using different linguistic means should typically be retrieved for the same queries. Information sought to answer a question needs to be recognized no matter how it is expressed. Semantic frames Fillmore 1982 Fillmore and Atkins 1992 address the paraphrase problem through their slot-and-filler templates representing frequently