Measuring Frame Relatedness

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 657–665, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Measuring frame relatedness Marco Pennacchiotti Yahoo! Inc. Santa Clara, CA 95054 pennac@yahoo-inc.com Michael Wirth Computational Linguistics Saarland University, Germany miwirth@coli.uni-sb.de Abstract In this paper we introduce the notion of “frame relatedness”, i.e. relatedness among prototypical situations as repre- sented in the FrameNet database. We first demonstrate the cognitive plausibility of that notion through an annotation experi- ment, and then propose different types of computational measures to automatically assess relatedness. Results show that our measures provide good performance on the task of ranking pairs of frames. 1 Introduction Measuring relatedness among linguistic entities is a crucial topic in NLP. Automatically assess- ing the degree of similarity or relatedness be- tween two words or two expressions, is of great help in a variety of tasks, such as Question An- swering, Recognizing Textual Entailment (RTE), Information Extraction and discourse processing. Since the very beginning of computational lin- guistics, many studies have been devoted to the definition and the implementation of automatic measures for word relatedness (e.g. (Ruben- stein and Goodenough, 1965; Resnik, 1995; Lin, 1998; Budanitsky and Hirst, 2006; Mohammad and Hirst, 2006)). More recently, relatedness between lexical-syntactic patterns has also been studied (Lin and Pantel, 2001; Szpektor et al., 2004), to support advanced tasks such as para- phrasing and RTE. Unfortunately, no attention has been paid so far to the definition of relatedness at the more abstract situational level – i.e. related- ness between two prototypical actions, events or state-of-affairs, taken out of context (e.g. the sit- uations of Killing and Death). A prominent defi- nition of “prototypical situation” is given in frame semantics (Fillmore, 1985), where a situation is modelled as a conceptual structure (a frame) con- stituted by the predicates that can evoke the situ- ation, and the semantic roles expressing the situa- tion’s participants. As measures of word relatedness help in discov- ering if two word occurrences express related con- cepts, so measures of frame relatedness should help to discover if two large text fragments are re- lated or talk about similar situations. Such mea- sures would be valuable in many tasks. For exam- ple, consider the following fragment, in the con- text of discourse processing: “In the 1950s the Shah initiated Iran ’s nu- clear research program and developed an ambi- tious plan to produce 23,000MW from nuclear power. The program was stopped by the Islamic Revolution in 1979, but it was revived later in the decade, when strategic interests began to drive the nuclear program.” The underlined words evoke highly related frames, namely ACTIVITY START, ACTIV- ITY STOP and CAUSE TO RESUME. This could suggest to link the three textual fragments associ- ated to the words, into a single coherent discourse unit, where the semantic roles of the different fragments can be easily mapped as co-referential (e.g. “Iran’s nuclear research program” - “The program” - “it”). Frame relatedness can also …

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