Two Approaches to Metaphor Detection Brian Macwhinney and Davida Fromm Carnegie Mellon University Pittsburgh PA, 15213 USA E-Mail: [email protected], [email protected]
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Two Approaches to Metaphor Detection Brian MacWhinney and Davida Fromm Carnegie Mellon University Pittsburgh PA, 15213 USA E-mail: [email protected], [email protected] Abstract Methods for automatic detection and interpretation of metaphors have focused on analysis and utilization of the ways in which metaphors violate selectional preferences (Martin, 2006). Detection and interpretation processes that rely on this method can achieve wide coverage and may be able to detect some novel metaphors. However, they are prone to high false alarm rates, often arising from imprecision in parsing and supporting ontological and lexical resources. An alternative approach to metaphor detection emphasizes the fact that many metaphors become conventionalized collocations, while still preserving their active metaphorical status. Given a large enough corpus for a given language, it is possible to use tools like SketchEngine (Kilgariff, Rychly, Smrz, & Tugwell, 2004) to locate these high frequency metaphors for a given target domain. In this paper, we examine the application of these two approaches and discuss their relative strengths and weaknesses for metaphors in the target domain of economic inequality in English, Spanish, Farsi, and Russian. Keywords: metaphor, corpora, grammatical relations, selectional restrictions, collocations • Action_Has_Modifier: verb-adverb pairs. 1. The Task (trap softly) Entity_Has_Modifier: noun-adjective pairs. In our project, code-named METAL, the task was to • (grinding poverty) automatically detect metaphors in the target domain Entity_Entity: noun-noun pairs. (Kövecses, 2002) of economic inequality – a subject of • particular current interest in the wake of the financial (wealth ladder) Entity_Has_Entity: noun-noun pairs for possession. collapse of 2008 and growing income disparity within • (poverty s trap, burden of poverty) and between countries caused by globalization and ’ Entity_Is_Entity: noun-noun pairs for identity. automation. Within this target domain, we defined the • (money is the lifeblood) four subdomains of poverty, wealth, taxes, and social Entity_Prep_Entity: noun-(prep)-noun pairs. class. The task of the system was to take short passages • discussing this issue, and to identify within each passage (path_to wealth) Once a checkable was detected in the parse tree, it was the metaphor(s) dealing with one of the target then sent on to four methods for evaluating selectional subdomains of economic inequality, if there was one. restrictions or preferences. As an example, consider the Once a sentence with a metaphor was detected, the sentence: There are many hyenas feeding on DBKL’s system had to delineate the specific words involved in money. In the relevant GR, feeding is the action and the metaphor. Finally, the system had to assign an interpretation to the metaphor by specifying the relevant money is the object in the Action_Has_Object relation. Because one does not literally feed on money, this pair is subsegment of the economic inequality target domain judged to violate a selectional restriction and hence be a (poverty, wealth, taxes, or social class), as well as the likely candidate for detection as a metaphor. source domain. The source domain was characterized in terms of conceptual metaphor theory (Lakoff & Johnson, 1980) involving sources such as PATH, CONTAINER, 2. Selectional Restriction Methods STRUCTURE, and BODY. The first selectional restriction method used the Common Semantic Features (CSF) method (Tsvetkov, The system targeted detection of metaphors in English, Boytsov, Gershman, Nyberg, & Dyer, 2014). For this Farsi, Russian, and Spanish. All passages in the system system, a classifier for metaphor detection was trained were processed using the TurboParser (Martins, Smith, on English samples and then applied to each of the four Xing, Aguiar, & Figueiredo, 2010) which produced target languages. The CSF classifier used coarse-grained dependency relations in CoNNL format (Kübler, semantic features derived from WordNet (Fellbaum, McDonald, & Nivre, 2009). From these parses, we 1998), psycholinguistic features for abstractness from the extracted pairs of words that represented specific MRC database (Wilson, 1988) and Word Representations grammatical relations (GRs) or “checkables” that could in Global Context (Huang, Socher, Manning, & Ng., then be processed for violations of selectional 2012). Training relied on the TroFi example base (Birke restrictions or preferences (Peters & Wilks, 2010; Wilks, & Sarkar, 2007) as parsed into dependency relations by 1978). In many cases the checkable included a word the TurboParser. Because the WordNet and other from the source domain linked to a word from the target materials were based on English, this method treated all domain. The possible checkables included: four languages as roughly equivalent. • Action_Has_Subject: verb-subject pairs. (poverty vanishes) The second selectional restriction method, which we will • Action_Has_Object: verb-object pairs. call the TRIPS method, attempted to extract additional (poverty traps) information from WordNet to achieve detection through 2501 preference violation (Wilks, 1978). The ontological classification system of WordNet was further elaborated We also explored ways of addressing this problem by by ontological relations deriving from the TRIPS system harvesting complete collections of figurative language (Allen, Swift, & de Beaumont, 2008) and the method of from the Oxford English Dictionary (OED) or the selecting likely figurative synsets from WordNet (Peters McGraw-Hill Dictionary of English Idioms and Phrasal & Wilks, 2010). The TRIPS lexicon provides Verbs (Spears, 2009). Our plan was to use these information on semantic roles and selectional conventionalized forms as a filter on the results from the restrictions, and the parser can construct the required selectional methods. However, before we completed semantic structures from WordNet entries. TRIPS has implementation of this method, we developed a more been shown to be successful at parsing WordNet glosses precise method, as described in the next section. in order to build commonsense knowledge bases (Allen et al., 2011). Consider how the system decides how to 4. Corpus-based Analysis handle a word like cooperate that is not in the TRIPS After working for nearly two years with the selectional hierarchy. Within WordNet, it tries unsuccessfully to methods, we then implemented an alternative approach process the first synset about collaborate, but then to metaphor detection that relied on corpus analysis, moves to the second synset which provides the item rather than detection of selectional restrictions. This work and then maps this to the TRIPS ontology to derive method used the TenTen corpora for English, Russian the correct selectional restrictions. and Spanish (Jakubicek, Kilgariff, Kovar, Rychly, & Suchomel, 2013) available from SketchEngine The third selectional restriction method relied on the (sketchengine.co.uk). These corpora are called TenTen matching of the lexical frames for verbs from VerbNet corpora, because they contain over 10 billion words (Baker, Filmore, & Cronin, 2003) along with features each. In addition, they have been lemmatized and tagged derived from WordNet. This method provides acceptable for part of speech and grammatical relations. precision, but its coverage (recall) is limited to Grammatical relations between the lemmas are metaphors that include verbs included in VerbNet. constructed using a SketchEngine (SkE) shallow parsing Moreover, because VerbNet is based on English verbs, grammar based on a set of regular expressions the extensions to the other languages are incomplete. formulated over sequences of part of speech categories. SkE grammars were already available within SkE for The fourth selectional restriction method processed Russian, Spanish, and English. These grammars were WordNet features using the Scone ontological database already built into the corpora, allowing for immediate (http://cs.cmu.edu/~sef/scone). The Scone ontological creation of WordSketches, as described below. Ivanova hierarchy establishes “split sets” such as et al. discuss how a SkE grammar was built for German animate/inanimate or tangible/intangible. Given a and Khokhlova and Zakharov (2010) describe the collocation such as green ideas, Scone would require that construction of a SkE grammar for Russian. For Farsi, only tangible things can have color and therefore green we built a SkE grammar from scratch, using the methods ideas must be metaphorical. described in these papers and in the SkE documentation. 3. Problems with Selectional Restrictions For Farsi, no TenTen corpus was available, so we needed The four selectional restriction methods relied on to build our own SkE corpus. We did this by using WordNet ontology and the method of checking for passages from web searches supplied by Shlomo selectional or preference violations across grammatical Argamon of IIT and pointers to relevant URLs from Ron relations. Thus, reliable detection of metaphors depended Ferguson of SAIC. Combining these materials into a on first being able to use NLP tools in each language for single corpus, we then lemmatized and tagged this new accurate identification of each of the relevant corpus, using the methods described by Feely et al. “checkable” relations. Whenever there were errors in this (2014). Using the SkE documentation, we then wrote a parsing process, the resultant grammatical relations or SkE grammar to detect the most important grammatical