F-Score Driven Max Margin Neural Network for Named Entity
            Recognition in Chinese Social Media

He, Hangfeng; Sun, Xu

doi:10.18653/v1/e17-2113

Cited by 107 publications

(59 citation statements)

References 19 publications

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“…One significant drawback is that there is only a very small amount of annotated data available. Weibo NER dataset (Peng and Dredze, 2015;He and Sun, 2017a) and Sighan2006 NER dataset (Levow, 2006) are two widely used datasets for Chinese NER task, containing 1.3k and 45k training examples, respectively. On the two datasets, the highest F1 scores are 48.41% and 89.21%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

Cao

Chen

Liu

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

177

128

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Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the taskspecific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit selfattention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-ofthe-art methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

Cao

Chen

Liu

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

177

128

View full text Add to dashboard Cite

show abstract

“…Peng et al [17] utilize word boundary tags as features to provide richer information and improve the F1-score to 58.99%. He et al [8] propose a unified model for cross domain and improve F1-score to 58.23% from 54.82% [7]. Zhang et al [26] investigate a lattice network which explicitly leverages word and word sequence information, and achieve F1-score of 58.79%.…”

Section: Comparison With Previous Workmentioning

confidence: 99%

A Mixed Semantic Features Model for Chinese NER with Characters and Words

Chang

Jiang

et al. 2020

Lecture Notes in Computer Science

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Named Entity Recognition (NER) is an essential part of many natural language processing (NLP) tasks. The existing Chinese NER methods are mostly based on word segmentation, or use the character sequences as input. However, using a single granularity representation would suffer from the problems of out-of-vocabulary and word segmentation errors, and the semantic content is relatively simple. In this paper, we introduce the self-attention mechanism into the BiLSTM-CRF neural network structure for Chinese named entity recognition with two embedding. Different from other models, our method combines character and word features at the sequence level, and the attention mechanism computes similarity on the total sequence consisted of characters and words. The character semantic information and the structure of words work together to improve the accuracy of word boundary segmentation and solve the problem of long-phrase combination. We validate our model on MSRA and Weibo corpora, and experiments demonstrate that our model can significantly improve the performance of the Chinese NER task.

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“…The model (Peng and Dredze, 2016) that jointly trains NER and CWS reaches a F1-score of 58.99%. He and Sun (2017b) propose a unified model to exploit crossdomain and semi-supervised data, which improves the F1-score from 54.82% to 58.23% compared with the model proposed by He and Sun (2017a). Cao et al (2018) use an adversarial transfer learning framework to incorporate task-shared word boundary information from CWS and achieves a F1-score of 58.70%.…”

Section: Weibo Datasetmentioning

confidence: 99%

Untitled

Zhu¹,

Wang²

2019

Proceedings of the 2019 Conference of the North

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Named entity recognition (NER) is a common task in Natural Language Processing (NLP), but it remains more challenging in Chinese because of its lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually necessary as the first step for Chinese NER. However, models based on wordlevel embeddings and lexicon features often suffer from segmentation errors and out-ofvocabulary (OOV) problems. In this paper, we investigate a Convolutional Attention Network (CAN) for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Moreover, differently from other approaches, CAN-NER does not depend on any external resources like lexicons and employing small-size char embeddings makes CAN-NER more practical for real systems scenarios. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domains datasets.

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F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

Cited by 107 publications

References 19 publications

Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism

A Mixed Semantic Features Model for Chinese NER with Characters and Words

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