Sorting out the Most Confusing English Phrasal Verbs

First Joint Conference on Lexical and Computational Semantics (*SEM), pages 65–69, Montr´eal, Canada, June 7-8, 2012. c⃝2012 Association for Computational Linguistics Sorting out the Most Confusing English Phrasal Verbs Yuancheng Tu Department of Linguistics University of Illinois ytu@illinois.edu Dan Roth Department of Computer Science University of Illinois danr@illinois.edu Abstract In this paper, we investigate a full-fledged supervised machine learning framework for identifying English phrasal verbs in a given context. We concentrate on those that we de- fine as the most confusing phrasal verbs, in the sense that they are the most commonly used ones whose occurrence may correspond either to a true phrasal verb or an alignment of a sim- ple verb with a preposition. We construct a benchmark dataset1 with 1,348 sentences from BNC, annotated via an Inter- net crowdsourcing platform. This dataset is further split into two groups, more idiomatic group which consists of those that tend to be used as a true phrasal verb and more compo- sitional group which tends to be used either way. We build a discriminative classifier with easily available lexical and syntactic features and test it over the datasets. The classifier overall achieves 79.4% accuracy, 41.1% er- ror deduction compared to the corpus major- ity baseline 65%. However, it is even more interesting to discover that the classifier learns more from the more compositional examples than those idiomatic ones. 1 Introduction Phrasal verbs in English, are syntactically defined as combinations of verbs and prepositions or parti- cles, but semantically their meanings are generally not the direct sum of their parts. For example, give in means submit, yield in the sentence, Adam’s say- ing it’s important to stand firm , not give in to ter- rorists. Adam was not giving anything and he was 1http://cogcomp.cs.illinois.edu/page/resources/PVC Data not in anywhere either. (Kolln and Funk, 1998) uses the test of meaning to detect English phrasal verbs, i.e., each phrasal verb could be replaced by a single verb with the same general meaning, for example, using yield to replace give in in the aforementioned sentence. To confuse the issue even further, some phrasal verbs, for example, give in in the follow- ing two sentences, are used either as a true phrasal verb (the first sentence) or not (the second sentence) though their surface forms look cosmetically similar. 1. How many Englishmen gave in to their emo- tions like that ? 2. It is just this denial of anything beyond what is directly given in experience that marks Berke- ley out as an empiricist . This paper is targeting to build an automatic learner which can recognize a true phrasal verb from its orthographically identical construction with a verb and a prepositional phrase. Similar to other types of MultiWord Expressions (MWEs) (Sag et al., 2002), the syntactic complexity and semantic id- iosyncrasies of phrasal verbs pose many particular challenges in empirical Natural Language Process- ing (NLP). Even though a few of previous works have explored this identification problem empiri- cally (Li et al., 2003; Kim and Baldwin, …

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