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

Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen, Knowledge Graph and Text Jointly Embedding, in The 2014 Conference on Empirical Methods on Natural Language Processing, ACL – Association for Computational Linguistics, October 2014

Zhenghao Wang, Shengquan Yan, Huaming Wang, and Xuedong Huang, An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark, no. MSR-TR-2014-121, 3 September 2014

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