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Geoffrey Zweig
Researcher at Microsoft
Publications - 90
Citations - 9719
Geoffrey Zweig is an academic researcher from Microsoft. The author has contributed to research in topics: Language model & Recurrent neural network. The author has an hindex of 34, co-authored 90 publications receiving 8853 citations.
Papers
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Proceedings Article
Linguistic Regularities in Continuous Space Word Representations
TL;DR: The vector-space word representations that are implicitly learned by the input-layer weights are found to be surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset.
Proceedings ArticleDOI
From captions to visual concepts and back
Hao Fang,Saurabh Gupta,Forrest Iandola,Rupesh Kumar Srivastava,Li Deng,Piotr Dollár,Jianfeng Gao,Xiaodong He,Margaret Mitchell,John Platt,C. Lawrence Zitnick,Geoffrey Zweig +11 more
TL;DR: This paper used multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives, which serve as conditional inputs to a maximum-entropy language model.
Proceedings ArticleDOI
Context dependent recurrent neural network language model
Tomas Mikolov,Geoffrey Zweig +1 more
TL;DR: This paper improves recurrent neural network language models performance by providing a contextual real-valued input vector in association with each word to convey contextual information about the sentence being modeled by performing Latent Dirichlet Allocation using a block of preceding text.
Journal ArticleDOI
Using recurrent neural networks for slot filling in spoken language understanding
Grégoire Mesnil,Yann N. Dauphin,Kaisheng Yao,Yoshua Bengio,Li Deng,Dilek Hakkani-Tur,Xiaodong He,Larry Heck,Gokhan Tur,Dong Yu,Geoffrey Zweig +10 more
TL;DR: This paper implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants, and implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark.
Proceedings ArticleDOI
Spoken language understanding using long short-term memory neural networks
TL;DR: This paper investigates using long short-term memory (LSTM) neural networks, which contain input, output and forgetting gates and are more advanced than simple RNN, for the word labeling task and proposes a regression model on top of the LSTM un-normalized scores to explicitly model output-label dependence.