Inducing Frame Semantic Verb Classes from WordNet and LDOCE

Inducing Frame Semantic Verb Classes from WordNet and LDOCE Rebecca Green, *†‡ 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 language- based 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 occurring, structured experiences. Semantic frame types of an intermediate granularity have the potential to fulfill an interlingua role within a solution to the paraphrase problem. Until now, semantic frames have been generated by hand (as in Fillmore and Atkins, 1992), based on native speaker intuition; the FrameNet project (http://www.icsi.berkeley.edu/ ~framenet; Johnson et al., 2002) now couples this generation with empirical validation. Only recently has this project begun to achieve relative breadth in its inventory of semantic frames. To have a comprehensive inventory of semantic frames, however, we need the capacity to generate semantic frames semi-automatically (the need for manual post-editing is assumed). To address these challenges, we have developed SemFrame, a system that induces semantic frames automatically. Overall, the system performs two primary functions: (1) identification of sets of verb senses that evoke a common semantic frame (in the sense that lexical units call forth corresponding conceptual structures); and (2) identification of the conceptual structure of semantic frames. This paper explores the first task of identifying frame semantic verb classes. These classes have several types of uses. First, they are the basis for identifying the internal structure of the frame proper, as set forth in Green and Dorr, 2004. Second, they may be used to extend FrameNet. Third, they …

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