A Continuously Growing Dataset of Sentential Paraphrases

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at ~70% precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201

A Spoken Dialogue System for Enabling Comfortable Information Acquisition and Consumption

早大学位記番号:新8137早稲田大

言語学的特徴を用いた述部の正規化と同義性判定

京都大学0048新制・課程博士博士(情報学)甲第17991号情博第513号新制||情||91(附属図書館)80835京都大学大学院情報学研究科知能情報学専攻(主査)教授 黒橋 禎夫, 教授 石田 亨, 教授 河原 達也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA