The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
Open accessAbstract
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.
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Neural Architectures for Named Entity Recognition
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Supervised Sequence Labelling with Recurrent Neural Networks
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Named Entity Recognition with Bidirectional LSTM-CNNs
Chiu, J. P. C., & Nichols, E. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00104 · Cited by 1,909
A Fast and Accurate Dependency Parser using Neural Networks
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Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Marcheggiani, D., & Titov, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1159 · Cited by 907
Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications
Mienye, I. D., Swart, T. G., & Obaido, G. (2024) · Information · doi:10.3390/info15090517 · Cited by 732
Analysis Methods in Neural Language Processing: A Survey
Belinkov, Y., & Glass, J. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00254 · Cited by 477
Linguistically-Informed Self-Attention for Semantic Role Labeling
Strubell, E., Verga, P., Andor, D., Weiss, D., & McCallum, A. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1548 · Cited by 406
Deep Fisher discriminant learning for mobile hand gesture recognition
Li, C., Xie, C., Zhang, B., Chen, C., & Han, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.12.023 · Cited by 72
Metadata
- Authors
- Kiperwasser, E., & Goldberg, Y.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2016-12
- Volume
- 4
- Pages
- 313-327
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2301095666
- Cited by
- 615
- References
- 39
- FWCI
- 91.57
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Kiperwasser, E., & Goldberg, Y. (2016). Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00101
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