We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual parallel data. Our collection is extracted and ranked using state of the art methods. The multilingual PPDB has over a billion paraphrase pairs in total, covering the following languages: Arabic, Bulgari...

The validity of applying paraphrase rules depends on the domain of the text that they are being applied to. We develop a novel method for extracting domainspecific paraphrases. We adapt the bilingual pivoting paraphrase method to bias the training data to be more like our target domain of biology. Our best model results in higher precision while retaining complete recall, giving a 10% relative ...

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models — for entity disambiguation and for paraphrase detection — to overcome...

Paraphrasing constitutes a corner stone in many Natural Language Processing fields like monolingual text-to-text generation and automatic text summarization. Indeed, aligned monolingual corpora are likely to boost the learning process of text-to-text generation models. A Paraphrase learning strategy can be defined as a two-step process: (1) identifying and extracting related sentence pairs from...

One of the difficulties in Natural Language Processing is the fact that there are many way to express the same thing or event. These expressions are called “Paraphrases”. Paraphrase is important in applications such as IR, QA and IE, and one of the difficulties in paraphrase research is acquiring the requisite paraphrase knowledge. In this paper, we describe an unsupervised method to discover p...

This paper proposes a method that extracts paraphrases from search engine query logs. The method first extracts paraphrase query-title pairs based on an assumption that a search query and its corresponding clicked document titles may mean the same thing. It then extracts paraphrase query-query and title-title pairs from the query-title paraphrases with a pivot approach. Paraphrases extracted in...

We present MULTIP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter. We jointly model paraphrase relations between word and sentence pairs and assume only sentence-level annotations during learning. Using this principled latent variable model alone, we achieve the performance competitive with a state-of-the-art method whi...