{"type":"ScholarlyArticle","title":"Convolutional Neural Tensor Network Architecture for Community-Based Question Answering","authors":[{"name":"Xipeng Qiu","slug":"xipeng-qiu","given":"Xipeng","family":"Qiu","affiliation":""},{"name":"Xuanjing Huang","slug":"xuanjing-huang","given":"Xuanjing","family":"Huang","affiliation":""}],"abstract":"Retrieving similar questions is very important in community-based question answering. A major challenge is the lexical gap in sentence matching. In this paper, we propose a convolutional neural tensor network architecture to encode the sentences in semantic space and model their interactions with a tensor layer. Our model integrates sentence modeling and semantic matching into a single model, which can not only capture the useful information with convolutional and pooling layers, but also learn the matching metrics between the question and its answer. Besides, our model is a general architecture, with no need for the other knowledge such as lexical or syntactic analysis. The experimental results shows that our method outperforms the other methods on two matching tasks.","doi":null,"url":"https://ijcai.org/proceedings/2015/188","pdf":"https://ijcai.org/proceedings/2015/0188.pdf","markdown":"https://ijcai.org/proceedings/2015/188.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/188","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Natural Language Processing","track":"Main Track","number":188,"pages":{"first":1305,"last":1310},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}