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Semantic Role Labeling Using Recursive Neural Network

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (CCL 2015, NLP-NABD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9427))

Abstract

Semantic role labeling (SRL) is an important NLP task for understanding the semantic of sentences in real-world. SRL is a task which assigns semantic roles to different phrases in a sentence for a given word. We design a recursive neural network model for SRL. On the one hand, comparing to traditional shallow models, our model does not dependent on lots of rich hand-designed features. On the other hand, different from early deep models, our model is able to add many shallow features. Further more, our model uses global structure information of parse trees. In our experiment, we evaluate using the CoNLL-2005 data and reach a competitive performance with fewer features.

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Acknowledgments

This work is supported by National Key Basic Research Program of China (2014CB340504) and National Natural Science Foundation of China (61273318).

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Authors and Affiliations

  1. Key Laboratory of Computational Linguistics, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Tianshi Li & Baobao Chang

  2. Collaborative Innovation Center for Language Ability, Xuzhou, 221009, China

    Tianshi Li

Authors
  1. Tianshi Li
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  2. Baobao Chang
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Corresponding author

Correspondence to Baobao Chang .

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Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Tsinghua University, Beijing, China

    Zhiyuan Liu

  3. Soochow University, Suzhou, Jiangsu, China

    Min Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

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© 2015 Springer International Publishing Switzerland

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Li, T., Chang, B. (2015). Semantic Role Labeling Using Recursive Neural Network. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-25816-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25815-7

  • Online ISBN: 978-3-319-25816-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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