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.
Li Dong, Furu Wei, Shujie Liu, Ming Zhou, and Ke Xu, A Statistical Parsing Framework for Sentiment Classification, Computational Linguistics, December 2015.
Dilek Hakkani-Tur, Yun-Cheng Ju, Geoffrey Zweig, and Gokhan Tur, Clustering Novel Intents in a Conversational Interaction System with Semantic Parsing, Interspeech 2015 Conference, September 2015.
Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong, New Transfer Learning Techniques For Disparate Label Sets, in Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, 29 August 2015.
Young-Bum Kim, Karl Stratos, and Ruhi Sarikaya, Pre-training of Hidden-Unit CRFs, in Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, 28 August 2015.
Young-Bum Kim, Karl Stratos, Xiaohu Liu, and Ruhi Sarikaya, Compact Lexicon Selection with Spectral Methods, in Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, 27 August 2015.
- Language to Code
- From Captions to Visual Concepts and Back
- Data-Driven Conversation
- NLPwin parses AMR
- Deep Learning for Natural Language Processing: Theory and Practice (CIKM2014 Tutorial)
- Colloquial to Arabic Converter
- Part of Speech (POS) Tagger
- Named Entity Recognizer (NER)
- SARF (morphological analyzer)
- Arabic Toolkit Service (ATKS)
- Catalyst: Center for Sustainable Development
- Lexical Semantics Toolkit & Dataset
- Meeting Recognition and Understanding
- Spoken Language Understanding