Large Scale Acquisition of Paraphrases for Learning Surface Patterns

Proceedings of ACL-08: HLT, pages 674–682, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Large Scale Acquisition of Paraphrases for Learning Surface Patterns Rahul Bhagat∗ Information Sciences Institute University of Southern California Marina del Rey, CA rahul@isi.edu Deepak Ravichandran Google Inc. 1600 Amphitheatre Parkway Mountain View, CA deepakr@google.com Abstract Paraphrases have proved to be useful in many applications, including Machine Translation, Question Answering, Summarization, and In- formation Retrieval. Paraphrase acquisition methods that use a single monolingual corpus often produce only syntactic paraphrases. We present a method for obtaining surface para- phrases, using a 150GB (25 billion words) monolingual corpus. Our method achieves an accuracy of around 70% on the paraphrase ac- quisition task. We further show that we can use these paraphrases to generate surface pat- terns for relation extraction. Our patterns are much more precise than those obtained by us- ing a state of the art baseline and can extract relations with more than 80% precision for each of the test relations. 1 Introduction Paraphrases are textual expressions that convey the same meaning using different surface words. For ex- ample consider the following sentences: Google acquired YouTube. (1) Google completed the acquisition of YouTube. (2) Since they convey the same meaning, sentences (1) and (2) are sentence level paraphrases, and the phrases “acquired” and “completed the acquisition of” in (1) and (2) respectively are phrasal para- phrases. Paraphrases provide a way to capture the vari- ability of language and hence play an important ∗Work done during an internship at Google Inc. role in many natural language processing (NLP) ap- plications. For example, in question answering, paraphrases have been used to find multiple pat- terns that pinpoint the same answer (Ravichandran and Hovy, 2002); in statistical machine transla- tion, they have been used to find translations for unseen source language phrases (Callison-Burch et al., 2006); in multi-document summarization, they have been used to identify phrases from different sentences that express the same information (Barzi- lay et al., 1999); in information retrieval they have been used for query expansion (Anick and Tipirneni, 1999). Learning paraphrases requires one to ensure iden- tity of meaning. Since there are no adequate se- mantic interpretation systems available today, para- phrase acquisition techniques use some other mech- anism as a kind of “pivot” to (help) ensure semantic identity. Each pivot mechanism selects phrases with similar meaning in a different characteristic way. A popular method, the so-called distributional simi- larity, is based on the dictum of Zelig Harris “you shall know the words by the company they keep”: given highly discriminating left and right contexts, only words with very similar meaning will be found to fit in between them. For paraphrasing, this has been often used to find syntactic transformations in parse trees that preserve (semantic) meaning. An- other method is to use a bilingual dictionary or trans- lation table as pivot mechanism: all source language words or phrases that translate to a given foreign word/phrase are deemed to be paraphrases of one another. In …

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