Identifying Phrasal Verbs Using Many Bilingual Corpora

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 636–646, Seattle, Washington, USA, 18-21 October 2013. c⃝2013 Association for Computational Linguistics Identifying Phrasal Verbs Using Many Bilingual Corpora Karl Pichotta∗ Department of Computer Science University of Texas at Austin pichotta@cs.utexas.edu John DeNero Google, Inc. denero@google.com Abstract We address the problem of identifying mul- tiword expressions in a language, focus- ing on English phrasal verbs. Our poly- glot ranking approach integrates frequency statistics from translated corpora in 50 dif- ferent languages. Our experimental eval- uation demonstrates that combining statisti- cal evidence from many parallel corpora us- ing a novel ranking-oriented boosting algo- rithm produces a comprehensive set of English phrasal verbs, achieving performance compa- rable to a human-curated set. 1 Introduction A multiword expression (MWE), or noncomposi- tional compound, is a sequence of words whose meaning cannot be composed directly from the meanings of its constituent words. These idiosyn- cratic phrases are prevalent in the lexicon of a lan- guage; Jackendoff (1993) estimates that their num- ber is on the same order of magnitude as that of sin- gle words, and Sag et al. (2002) suggest that they are much more common, though quantifying them is challenging (Church, 2011). The task of identify- ing MWEs is relevant not only to lexical semantics applications, but also machine translation (Koehn et al., 2003; Ren et al., 2009; Pal et al., 2010), informa- tion retrieval (Xu et al., 2010; Acosta et al., 2011), and syntactic parsing (Sag et al., 2002). Awareness of MWEs has empirically proven useful in a num- ber of domains: Finlayson and Kulkarni (2011), for example, use MWEs to attain a significant perfor- mance improvement in word sense disambiguation; Venkatapathy and Joshi (2006) use features associ- ated with MWEs to improve word alignment. ∗Research conducted during an internship at Google. We focus on a particular subset of MWEs, English phrasal verbs. A phrasal verb consists of a head verb followed by one or more particles, such that the meaning of the phrase cannot be determined by combining the simplex meanings of its constituent words (Baldwin and Villavicencio, 2002; Dixon, 1982; Bannard et al., 2003).1 Examples of phrasal verbs include count on [rely], look after [tend], or take off [remove], the meanings of which do not in- volve counting, looking, or taking. In contrast, there are verbs followed by particles that are not phrasal verbs, because their meaning is compositional, such as walk towards, sit behind, or paint on. We identify phrasal verbs by using frequency statistics calculated from parallel corpora, consist- ing of bilingual pairs of documents such that one is a translation of the other, with one document in English. We leverage the observation that a verb will translate in an atypical way when occurring as the head of a phrasal verb. For example, the word look in the context of look after will tend to trans- late differently from how look translates generally. In order to characterize this difference, we calculate a frequency distribution over …

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