Proceedings

Syntax-Based Deep Matching of Short Texts

Mingxuan Wang, Zhengdong Lu, Hang Li, Qun Liu

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1354-1360.

Abstract

Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts. We propose a new approach to the problem, called Deep Match Tree (DeepMatch_tree), under a general setting. The approach consists of two components, 1) a mining algorithm to discover patterns for matching two short-texts, defined in the product space of dependency trees, and 2) a deep neural network for matching short texts using the mined patterns, as well as a learning algorithm to build the network having a sparse structure. We test our algorithm on the problem of matching a tweet and a response in social media, a hard matching problem proposed in [Wang et al., 2013], and show that DeepMatch_tree can outperform a number of competitor models including one without using dependency trees and one based on word-embedding, all with large margins.

Mingxuan Wang, Zhengdong Lu, Hang Li, and Qun Liu. 2015. Syntax-Based Deep Matching of Short Texts. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 1354–1360. https://ijcai.org/proceedings/2015/195
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards