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Chapitre D'ouvrage Année : 2021

Named Entity Recognition Architecture Combining Contextual and Global Features

Résumé

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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Dates et versions

hal-03480557 , version 1 (14-12-2021)

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Tran Thi Hong Hanh, Antoine Doucet, Nicolas Sidere, Jose G. Moreno, Senja Pollak. Named Entity Recognition Architecture Combining Contextual and Global Features. Hao-Ren Ke; Chei Sian Lee; Kazunari Sugiyama. Towards Open and Trustworthy Digital Societies. 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Virtual Event, December 1–3, 2021, Proceedings, 13133, Springer, pp.264-276, 2021, Lecture Notes in Computer Science, 978-3-030-91668-8. ⟨10.1007/978-3-030-91669-5_21⟩. ⟨hal-03480557⟩
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