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Named Entity Recognition Architecture Combining Contextual and Global Features

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Towards Open and Trustworthy Digital Societies (ICADL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13133))

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Abstract

Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, ...) within a document into predefined categories. Correctly identifying these phrases plays a significant role in simplifying information access. However, it remains a difficult task because named entities (NEs) have multiple forms and they are context dependent. While the context can be represented by contextual features, the global relations are often misrepresented by those models. In this paper, we propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-used dataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art (SOTA).

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Notes

  1. 1.

    Link to the code: github.com/honghanhh/ner-combining-contextual-and-global-features.

  2. 2.

    https://fasttext.cc/.

  3. 3.

    https://github.com/flairNLP/flair.

  4. 4.

    https://github.com/stanfordnlp/stanza.

  5. 5.

    https://github.com/zihangdai/xlnet.

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Acknowledgements

This work has been supported by the European Union’s Horizon 2020 research and innovation program under grants 770299 (NewsEye) and 825153 (EMBEDDIA). The work of S. P. has also received financial support from the Slovenian Research Agency for research core funding for the Knowledge Technologies programme (No. P2-0103) and the project CANDAS (No. J6-2581).

Author information

Authors and Affiliations

  1. University of Science and Technology in Hanoi, Hanoi, Vietnam

    Tran Thi Hong Hanh

  2. L3i Laboratory, University of La Rochelle, La Rochelle, France

    Tran Thi Hong Hanh, Antoine Doucet & Nicolas Sidere

  3. IRIT, University of Toulouse, Toulouse, France

    Jose G. Moreno

  4. Jozef Stefan Institute, Ljubljana, Slovenia

    Tran Thi Hong Hanh & Senja Pollak

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  1. Tran Thi Hong Hanh
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Correspondence to Senja Pollak .

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Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Hao-Ren Ke

  2. Nanyang Technological University, Singapore, Singapore

    Chei Sian Lee

  3. Kyoto University, Kyoto, Japan

    Kazunari Sugiyama

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Hanh, T.T.H., Doucet, A., Sidere, N., Moreno, J.G., Pollak, S. (2021). Named Entity Recognition Architecture Combining Contextual and Global Features. In: Ke, HR., Lee, C.S., Sugiyama, K. (eds) Towards Open and Trustworthy Digital Societies. ICADL 2021. Lecture Notes in Computer Science(), vol 13133. Springer, Cham. https://doi.org/10.1007/978-3-030-91669-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-91669-5_21

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