The PPDB is an automatically built database which contains millions of paraphrases in different languages. Paraphrases in this resource are associated with features that serve to their ranking and reflect paraphrase quality. This context-unaware ranking captures the semantic similarity of paraphrases but cannot serve to estimate their adequacy in specific contexts. We propose to use vector-spac...

While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. Our approach yields phrasal and single word lexical paraphrases as well as sy...

Previous work has shown that high quality phrasal paraphrases can be extracted from bilingual parallel corpora. However, it is not clear whether bitexts are an appropriate resource for extracting more sophisticated sentential paraphrases, which are more obviously learnable from monolingual parallel corpora. We extend bilingual paraphrase extraction to syntactic paraphrases and demonstrate its a...

A variety of applications can benefit from broad and detailed repositories of linguistic and world knowledge. An emerging approach to acquiring such repositories is to collect them from volunteer contributors. To increase the volume of contributions, some deployed systems for collecting volunteer-contributed knowledge offer recognition or prizes to those who provide the highest volume of contri...

Visually grounded paraphrases (VGPs) are different phrasal expressions describing the same visual concept in an image. Previous studies treat VGP identification as a binary classification task, which ignores various phenomena behind VGPs (i.e., linguistic interpretation of concept) such and from aspects. In this paper, we propose semantic typology for VGPs, aiming to elucidate deepen understand...