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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

Hang Yan , Bocao Deng , Xiaonan Li , Xipeng Qiu This is my paper · ORCID
classification cs.CLcs.LG
keywords encodertransformertasksattentionentityfeaturesnamedother
verification ladder T0 review T1 audit T2 compute T3 formal
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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Open-source smaller LLMs show moderate correlation with human OPTION12 SDM coding, with Gemma3:12b performing best, but remain insufficient for fully automated clinical assessment.

  2. Small Language Model Makes an Effective Long Text Extractor

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A smaller span-based NER model with a compressed plus-shaped attention mechanism extracts long entities from very long texts with less memory than prior span-based methods.

  3. A Word is Worth 4-bit: Efficient Log Parsing with Binary Coded Decimal Recognition

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A 312k-parameter character-level log parser using 4-bit BCD encoding matches LLM-based parser accuracy at up to 20x higher throughput.

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