Named Entity Recognition for Chinese biomedical patents

Hu, Yuting; Verberne, Suzan

doi:10.18653/v1/2020.coling-main.54

Cited by 20 publications

(11 citation statements)

References 16 publications

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“…BERT-BiLSTM-CRF (Huang et al, 2015) is an established model for sequence tagging (and the state-of-the-art for name entity recognition in different languages [Huang et al, 2015;Hu and Verberne, 2020]), which uses a BiLSTM to encode the sequence information and then performs sequence tagging with a conditional random field (CRF).…”

Section: Methodsmentioning

confidence: 99%

Idiomatic Expression Identification using Semantic Compatibility

Zeng

Bhat

2021

Transactions of the Association for Computational Linguistics

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Idiomatic expressions are an integral part of natural language and constantly being added to a language. Owing to their non-compositionality and their ability to take on a figurative or literal meaning depending on the sentential context, they have been a classical challenge for NLP systems. To address this challenge, we study the task of detecting whether a sentence has an idiomatic expression and localizing it when it occurs in a figurative sense. Prior research for this task has studied specific classes of idiomatic expressions offering limited views of their generalizability to new idioms. We propose a multi-stage neural architecture with attention flow as a solution. The network effectively fuses contextual and lexical information at different levels using word and sub-word representations. Empirical evaluations on three of the largest benchmark datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality show that our proposed model achieves new state-of-the-art results. A salient feature of the model is its ability to identify idioms unseen during training with gains from 1.4% to 30.8% over competitive baselines on the largest dataset.

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Section: Methodsmentioning

confidence: 99%

Idiomatic Expression Identification using Semantic Compatibility

Zeng

Bhat

2021

Transactions of the Association for Computational Linguistics

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“…However, incorrect word segmentation will result in propagation errors in entity detection, and purely char-based approach will miss the word information. Pre-trained language models, such as BERT, can generate contextual embedding which can outperform other character or word-based approaches (Hu et al, 2020). More importantly, BPE subword segmentation method is employed by BERT-based models and word-level information is not explicitly modeled.…”

Section: Chinese Nermentioning

confidence: 99%

OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge

Chen¹,

Wang²,

Zheng³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER. First, we explore the performance of model-based approaches and their ensemble, finding that fine-tuning the pre-trained Chinese RoBERTa-wwm model with word semantic representation and contextual gazetteer representation performs best among single models. However, the model-based approach performs poorly on test data because of low-context and unseen-entity cases. Then, we extend our system into two stages: (1) generating entity candidates by using neural model, soft-templates and Wikipedia lexicon. (2) predicting the final entity results within a feature-based rank model. For the evaluation, our best submission achieves an F 1 score of 0.7954 and attains the third-best score in the Chinese sub-track.

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“…Yang (2019) simply added a softmax on BERT, achieving state-of-the-art performance on CWS. Meng et al (2019); Hu and Verberne (2020) showed that models using the character features from BERT outperform the static embedding-based approaches by a large margin for Chinese NER and Chinese POS tagging.…”

Section: Related Workmentioning

confidence: 99%

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter

Liu¹,

Fu²,

Zhang³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

106

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Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labeling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labeling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves state-ofthe-art results.

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Named Entity Recognition for Chinese biomedical patents

Cited by 20 publications

References 16 publications

Idiomatic Expression Identification using Semantic Compatibility

Idiomatic Expression Identification using Semantic Compatibility

OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter

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