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Deep Learning in Natural Language Processing pp 79–116Cite as

Deep Learning in Lexical Analysis and Parsing

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Abstract

Lexical analysis and parsing tasks model the deeper properties of the words and their relationships to each other. The commonly used techniques involve word segmentation, part-of-speech tagging and parsing. A typical characteristic of such tasks is that the outputs are structured. Two types of methods are usually used to solve these structured prediction tasks: graph-based methods and transition-based methods. Graph-based methods differentiate output structures based on their characteristics directly, while transition-based methods transform output construction processes into state transition processes, differentiating sequences of transition actions. Neural network models have been successfully used for both graph-based and transition-based structured prediction. In this chapter, we give a review of applying deep learning in lexical analysis and parsing, and compare with traditional statistical methods.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Part-of-speech_tagging.

  2. 2.

    From Joakim Nivre’s tutorial at COLING-ACL, Sydney 2006.

  3. 3.

    http://www.ltp.ai.

  4. 4.

    http://money.cnn.com/2017/04/14/technology/uber-financials/.

  5. 5.

    https://en.wikipedia.org/wiki/CYK_algorithm.

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Wanxiang Che

  2. Singapore University of Technology and Design, Singapore, Singapore

    Yue Zhang

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  1. Wanxiang Che
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  1. AI Research at Citadel , Chicago, Illinois, USA

    Li Deng

  2. Tsinghua University , Beijing, China

    Yang Liu

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Che, W., Zhang, Y. (2018). Deep Learning in Lexical Analysis and Parsing. In: Deng, L., Liu, Y. (eds) Deep Learning in Natural Language Processing. Springer, Singapore. https://doi.org/10.1007/978-981-10-5209-5_4

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