Computational Linguistics

Machine translation, multilingual systems, and natural-language processing


Areas of focus for Microsoft Research’s inquiries into computational linguistics are threefold: machine translation, to create systems and technologies that cater to today’s multitude of translation scenarios; multilingual systems, to develop a natural-language-neutral approach to all aspects of linguistic computing; and natural-language processing, to design and build software that will analyze, understand, and generate languages that humans use naturally, with the goal of enabling a user to address a computer as though addressing another person.

 

Publications

Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , CIKM, November 2014

Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen, Modeling Interestingness with Deep Neural Networks, EMNLP, October 2014

Kai-Wei Chang, Wen-tau Yih, Bishan Yang, and Christopher Meek, Typed Tensor Decomposition of Knowledge Bases for Relation Extraction, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, ACL – Association for Computational Linguistics, October 2014

Xinying Song, Xiaodong He, Jianfeng Gao, and Li Deng, Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model, no. MSR-TR-2014-109, August 2014

Jiajun Zhang, Shujie Liu, Mu Li, Ming Zhou, and Chengqing Zong, Mind the Gap: Machine Translation by Minimizing the Semantic Gap in Embedding Space, AAAI - Association for the Advancement of Artificial Intelligence, July 2014

More publications...