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Neural Architectures for Named Entity Recognition

• Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer
• HLT-NAACL
• 2016

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that… (More)

Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling

• Kazuya Kawakami, Chris Dyer, Phil Blunsom
• ACL
• 2017

Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level… (More)

Learning to Represent Words in Context with Multilingual Supervision

• Kazuya Kawakami, Chris Dyer
• ArXiv
• 2015

We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for,… (More)

Character Sequence Models for Colorful Words

• Kazuya Kawakami, Chris Dyer, Bryan R. Routledge, Noah A. Smith
• EMNLP
• 2016

We present a neural network architecture to predict a point in color space from the sequence of characters in the color’s name. Using large scale color–name pairs obtained from an online color design… (More)

Inferring win-lose product network from user behavior

• Shuhei Iitsuka, Kazuya Kawakami, Seigen Hagiwara, Takayoshi Kawakami, Takayuki Hamada, Yutaka Matsuo
• WI
• 2017

Various data mining techniques to extract product relations have been examined, especially in the context of building intelligent recommender systems. Most such techniques, however, specifically… (More)

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