Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Article Swipe

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Computer science Dependency grammar Parsing Security token Artificial intelligence Dependency (UML) Sentence Feature (linguistics) LR parser Natural language processing Simple (philosophy) Feature engineering Context (archaeology) Feature vector Parser combinator Scheme (mathematics) Deep learning Linguistics Mathematics Computer security Epistemology Philosophy Mathematical analysis Biology Paleontology

Eliyahu Kiperwasser , Yoav Goldberg ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.1162/tacl_a_00101 · OA: W2301095666

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.