Metaphor Identification in Large Texts Corpora

Abstract

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus.

Dimensions of Metaphorical Meaning

“…Type III, contrary to (Neuman et al, 2013)). Such differences may be due to differences in data sets, as well as different syntactic models.…”

“…Focusing on word class of figurative expressions, so-called content words, such as nouns, adjectives and verbs, have long been considered to more strongly convey figurative meanings than so-called function words, such as prepositions (Neuman et al, 2013;Tsvetkov et al, 2013). Yet, Steen et al (2010) find prepositions within figurative expressions to be as prevalent as content words such as nouns and verbs, and indeed, for particular genres (such as academic texts) prepositions are the most frequently attested part of speech for figurative expressions.…”

“…phrase, clause, sentence). For example, Neuman et al (2013) investigate how metaphorical expressions apparently pattern by syntactically definable types, specifically: Type I, where "a subject noun is associated with an object noun via a form of the copula verb to be" (e.g. "God is a king"), Type II having the verb as "the focus of the metaphorical use representing the act of a subject noun on an object noun" (e.g.…”

Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia

“…It is an emerging field in NLP, with research still in relatively early stages. A variety of different machine-learning and statistical methods have been applied to the task, including clustering (Birke and Sarkar, 2006;Shutova et al, 2010;Shutova and Sun, 2013); topic models (Bethard et al, 2009;Heintz et al, 2013); topical structure and imageability analysis (Strzalkowski et al, 2013); semantic similarity graphs , and feature-based classifiers (Gedigian et al, 2006;Li and Sporleder, 2009;Turney et al, 2011;Dunn, 2013a,b;Hovy et al, 2013;Mohler et al, 2013;Neuman et al, 2013;Tsvetkov et al, 2013Tsvetkov et al, , 2014Klebanov et al). Metaphor detection methods differ in how they define the task of metaphor detection-for instance, some algorithms seek to determine whether a phrase (such as sweet victory) is metaphorical (Krishnakumaran and Zhu, 2007;Turney et al, 2011;Tsvetkov et al, 2014;Bracewell et al, 2014;Gutiérrez et al, 2016), while others attempt to tag metaphoricity at the level of the utterance (Dunn, 2013a), or at the level of individual tokens in running text (Klebanov et al;Schulder and Hovy, 2014;Do Dinh and Gurevych, 2016).…”

Evaluation of Metaphor Identification Procedure Vu (Mipvu) by the Criteria of a Truly Scientific Method

“…: она прогло-тила книгу -она проглотила пирожное. По мне-нию Г. Стейна, когнитивная лингвистика рас-сматривает метафору в языке как отражение ме-тафоры в сознании: метафора необходима нам, чтобы проецировать концептуальные структуры из относительно более конкретных, простых и известных областей на области более абстракт-ные, сложные и неизвестные [Steen 2014]. …”