Learning Hybrid Representations To Retrieve Semantically Equivalent Questions
PROCEEDINGS OF THE 53RD ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL) AND THE 7TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (IJCNLP), VOL 2(2015)
摘要
Retrieving similar questions in online Q&A community sites is a difficult task because different users may formulate the same question in a variety of ways, using different vocabulary and structure. In this work, we propose a new neural network architecture to perform the task of semantically equivalent question retrieval. The proposed architecture, which we call BOW-CNN, combines a bag-of-words (BOW) representation with a distributed vector representation created by a convolutional neural network (CNN). We perform experiments using data collected from two Stack Exchange communities. Our experimental results evidence that: (1) BOW-CNN is more effective than BOW based information retrieval methods such as TFIDF; (2) BOW-CNN is more robust than the pure CNN for long texts.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络