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Visualitza Documents OpenAIRE (Open Access Infrastructure for Research in Europe) per autoria "Dyer, Chris"

Ara mostrant els elements 1-5 de 5

Distilling an ensemble of greedy dependency parsers into one MST parser

Kuncoro, Adhiguna; Ballesteros, Miguel; Kong, Lingpeng; Dyer, Chris; Smith, Noah A. (ACL (Association for Computational Linguistics), 2016)

We introduce two first-order graph-based dependency parsers achieving a new state of the art. The first is a consensus parser built from an ensemble of independently trained greedy LSTM transition-based parsers with different ...
Improved transition-based parsing by modeling characters instead of words with LSTMs

Ballesteros, Miguel; Dyer, Chris; Smith, Noah A. (ACL (Association for Computational Linguistics), 2015)

We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory ...
Neural architectures for named entity recognition

Lample, Guillaume; Ballesteros, Miguel; Subramanian, Sandeep; Kawakami, Kazuya; Dyer, Chris (ACL (Association for Computational Linguistics), 2016)

State-of-the-art named entity recognition systems/nrely heavily on hand-crafted features and/ndomain-specific knowledge in order to learn/neffectively from the small, supervised training/ncorpora that are available. In ...
Transition-based dependency parsing with heuristic backtracking

Buckman, Jacob; Ballesteros, Miguel; Dyer, Chris (ACL (Association for Computational Linguistics), 2016)

We introduce a novel approach to the decoding problem in transition-based parsing: heuristic backtracking. This algorithm uses a series of partial parses on the sentence to locate the best candidate parse, using confidence ...
Transition-based dependency parsing with stack long short-term memory

Dyer, Chris; Ballesteros, Miguel; Ling, W; Matthews, A; Smith, Noah A. (ACL (Association for Computational Linguistics), 2015)

We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like ...