Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network

Sui, Dianbo; Chen, Yubo; Liu, Kang; Zhao, Jun; Liu, Shengping

doi:10.18653/v1/d19-1396

Cited by 117 publications

(62 citation statements)

References 39 publications

(60 reference statements)

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“…For supervised NER task, some researchers utilize lattice structure to incorporate the lexical information into character-based NER and avoid the segmentation error propagation of word Gui et al, 2019a;Xue et al, 2019b;Gui et al, 2019b;Sui et al, 2019). Additionally, gazetteers have long been regarded as a piece useful knowledge for NER, previous methods commonly incorporated gazetteers by either using them as handcraft features (Alan et al, 2011;Dominic et al, 2018) or auxiliary structural information (Ding et al, 2019;.…”

Section: Related Workmentioning

confidence: 99%

Coarse-to-Fine Pre-training for Named Entity Recognition

Mei¹,

Yu²,

Zhang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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More recently, Named Entity Recognition has achieved great advances aided by pre-training approaches such as BERT. However, current pre-training techniques focus on building language modeling objectives to learn a general representation, ignoring the named entityrelated knowledge. To this end, we propose a NER-specific pre-training framework to inject coarse-to-fine automatically mined entity knowledge into pre-trained models. Specifically, we first warm-up the model via an entity span identification task by training it with Wikipedia anchors, which can be deemed as general-typed entities. Then we leverage the gazetteer-based distant supervision strategy to train the model extract coarse-grained typed entities. Finally, we devise a self-supervised auxiliary task to mine the fine-grained named entity knowledge via clustering. Empirical studies on three public NER datasets demonstrate that our framework achieves significant improvements against several pre-trained baselines, establishing the new state-of-the-art performance on three benchmarks. Besides, we show that our framework gains promising results without using human-labeled training data, demonstrating its effectiveness in labelfew and low-resource scenarios. 1

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Section: Related Workmentioning

confidence: 99%

Coarse-to-Fine Pre-training for Named Entity Recognition

Mei¹,

Yu²,

Zhang³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…A drawback of the purely character-based NER model is that the word information is not fully exploited. To incorporate word information in Chinese NER, some recent methods, such as [10,11,12,13,14], resort to an automatically constructed lexicon.…”

Section: Chinese Nermentioning

confidence: 99%

Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

Wen

et al. 2021

Data Intelligence

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The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets and some other additional resources for these two subtasks were provided for participators. This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results. The pre-trained language models are widely applied in this evaluation task. Data argumentation and external resources are also helpful.

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“…A good representation of the input text is the key to obtain good model performance for many NLP tasks (Song et al, 2017;Sileo et al, 2019). Normally, a straightforward way to improve model performance is to enhance text representation by embeddings of extra features, which is demonstrated to be useful across tasks (Marcheggiani and Titov, 2017;Song et al, 2018a;Huang and Carley, 2019;Tian et al, 2020c), including NER Seyler et al, 2018;Sui et al, 2019;Gui et al, 2019b,a;Liu et al, 2019b). Among different types of extra features, the syntactic one has been proved to be helpful in previous studies for NER, where the effectiveness of POS labels, syntactic constituents, and dependency relations, are demonstrated by McCallum (2003), Li et al (2017), and Cetoli et al (2018), respectively.…”

Section: Syntactic Information Extractionmentioning

confidence: 99%

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

Nie¹,

Tian²,

Song³

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets. 1 * Equal contribution.

show abstract

Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network

Cited by 117 publications

References 39 publications

Coarse-to-Fine Pre-training for Named Entity Recognition

Coarse-to-Fine Pre-training for Named Entity Recognition

Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

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