Paraphrase-Sense-Tagged Sentences

Paraphrase-Sense-Tagged Sentences Anne Cocos and Chris Callison-Burch Department of Computer and Information Science University of Pennsylvania odonnell.anne@gmail.com, ccb@cis.upenn.edu Abstract Many natural language processing tasks re- quire discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a chal- lenge. We present a large resource of example usages for words having a particular mean- ing, called Paraphrase-Sense-Tagged Sentences (PSTS). Built on the premise that a word’s paraphrases instantiate its fine-grained mean- ings (i.e., bug has different meanings corre- sponding to its paraphrases fly and microbe) the resource contains up to 10,000 sentences for each of 3 million target-paraphrase pairs where the target word takes on the meaning of the paraphrase. We describe an automatic method based on bilingual pivoting used to enumerate sentences for PSTS, and present two models for ranking PSTS sentences based on their quality. Finally, we demonstrate the utility of PSTS by using it to build a dataset for the task of hypernym prediction in con- text. Training a model on this automatically generated dataset produces accuracy that is competitive with a model trained on smaller datasets crafted with some manual effort. 1 Introduction Word meaning is context-dependent. Whereas lex- ical semantic tasks like relation prediction have been studied extensively in a non-contextual set- ting, applying such models to a downstream task like textual inference or question answering re- quires taking the full context into account. For example, it may be true that rotavirus is a type of bug, but rotavirus is not within the realm of pos- sible answers to the question ‘‘Which bug caused the server outage?’’ Many tasks in natural language processing require discerning the meaning of polysemous words within a particular context. It can be a chal- lenge to develop corpora for training or evaluating sense-aware models, because particular attention must be paid to making sure the distribution of instances for a given word reflects its various meanings. This paper introduces Paraphrase- Sense-Tagged Sentences (PSTS),1a large resource of example usages of English words having a particular meaning. Rather than assume a rigid inventory of possible senses for each word, PSTS is grounded in the idea that the many fine-grained meanings of a word are instantiated by its para- phrases. For example, the word bug has different meanings corresponding to its paraphrases fly, error, and microbe, and PSTS includes sentences where bug takes on each of these meanings (Figure 1). Overall, the resource contains up to 10,000 sentences for each of roughly 3 million English lexical and phrasal paraphrases from the Paraphrase Database (PPDB) (Bannard and Callison-Burch, 2005; Ganitkevitch et al., 2013; Pavlick et al., 2015). PSTS was compiled by automatically extracting sentences from the English side of bilingual paral- lel corpora using a technique inspired by bilingual pivoting (Bannard and Callison-Burch, 2005). For instance, to find a sentence containing bug where it means fly, we select English sentences where bug is translated to the French mouche, Spanish mosca, or one of the other foreign words that …

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