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TENER: Adapting Transformer Encoder for Named Entity Recognition

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arxiv 1911.04474 v3 pith:HY3UOTRO submitted 2019-11-10 cs.CL cs.LG

TENER: Adapting Transformer Encoder for Named Entity Recognition

Hang Yan , Bocao Deng , Xiaonan Li , Xipeng Qiu This is my paper
classification cs.CL cs.LG
keywords encodertransformertasksattentionentityfeaturesnamedother
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer is broadly adopted in various Natural Language Processing (NLP) tasks owing to its parallelism and advantageous performance. Nevertheless, the performance of the Transformer in NER is not as good as it is in other NLP tasks. In this paper, we propose TENER, a NER architecture adopting adapted Transformer Encoder to model the character-level features and word-level features. By incorporating the direction and relative distance aware attention and the un-scaled attention, we prove the Transformer-like encoder is just as effective for NER as other NLP tasks.

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Cited by 2 Pith papers

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    cs.CL 2026-07 conditional novelty 5.0

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