Jun Ma, Yujie Zhang, Jinan Xu, Yufeng Chen (Beijing Jiaotong University, Beijing, 100044, China ) Abstract: Conventional “pivot” approach of acquiring paraphrases from bilingual corpus has limitations, where only candidated paraphrases within two steps are considered. In this paper, we propose a graph based model of acquiring paraphrases from phrases translation table. First, we describe a grap...

Automatically generated databases of English paraphrases have the drawback that they return a single list of paraphrases for an input word or phrase. This means that all senses of polysemous words are grouped together, unlike WordNet which partitions different senses into separate synsets. We present a new method for clustering paraphrases by word sense, and apply it to the Paraphrase Database ...

We present an approach for automatically learning paraphrases from aligned monolingual corpora. Our algorithm works by generalizing the syntactic paths between corresponding anchors in aligned sentence pairs. Compared to previous work, structural paraphrases generated by our algorithm tend to be much longer on average, and are capable of capturing long-distance dependencies. In addition to a st...

We describe a new unsupervised approach for synonymy discovery by aligning paraphrases in monolingual domain corpora. For that purpose, we identify phrasal terms that convey most of the concepts within domains and adapt a methodology for the automatic extraction and alignment of paraphrases to identify paraphrase casts from which valid synonyms are discovered. Results performed on two different...

In most applications of paraphrasing, contextual information should be considered since a word may have different paraphrases in different contexts. This paper presents a method that automatically acquires lexical contextspecific paraphrases from the web. The method includes two main stages, candidate paraphrase extraction and paraphrase validation. Evaluations were conducted on a news title co...