Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

Dong, Chuanhai; Wu, Huijia; Zhang, Jiajun; Zong, Chengqing

doi:10.1007/978-3-319-69005-6_17

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Due to space limit, here we only show the influence of the most important hyperparameter on our approach, i.e., λ in Eq. (6). The results are summarized in Figure 3.…”

Section: Influence Of Hyperparametersmentioning

confidence: 97%

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Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

Liu

et al. 2019

The World Wide Web Conference

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Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly contextdependent. In addition, Chinese texts lack delimiters to separate words, making it difficult to identify the boundary of entities. Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming. In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER. Second, we propose a unified framework to jointly train CNER and word segmentation models in order to enhance the ability of CNER model in identifying entity boundaries. Third, we introduce an automatic method to generate pseudo labeled samples from existing labeled data which can enrich the training data. Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient.

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“…Due to space limit, here we only show the influence of the most important hyperparameter on our approach, i.e., λ in Eq. (6). The results are summarized in Figure 3.…”

Section: Influence Of Hyperparametersmentioning

confidence: 97%

“…Compared with NER of English texts, Chinese NER is more difficult [6,19]. First, Chinese texts lack strong indications of entity names existing in English texts such as capitalization [27].…”

Section: Introductionmentioning

confidence: 99%

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

Liu

et al. 2019

The World Wide Web Conference

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“…If there is no CRF layer, the tags are independent of each other. In order to identify a large number of informal writing entities in Chinese social media, Dong Chuanhai et al [56] presented a multi-channel LSTM-CRF model based on out-of-domain annotation data, which utilizes different channels to share the same character embedding to improve NER performance. At the same time, choosing CRF as the decoder helps to enhance the recall rate.…”

Section: Model Frameworkmentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

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“…More studies on transfer learning have been successfully performed in the NLP sequence labelling tasks. Dong [29] proposed a multichannel DNN model to transfer knowledge cross-domain in Chinese social media. In order to ensure the consistency of the source and target domains, some tags are merged in their paper.…”

Section: Introductionmentioning

confidence: 99%

Multiple-level biomedical event trigger recognition with transfer learning

Chen

2019

BMC Bioinformatics

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Background Automatic extraction of biomedical events from literature is an important task in the understanding biological systems, allowing for faster update of the latest discoveries automatically. Detecting trigger words which indicate events is a critical step in the process of event extraction, because following steps depend on the recognized triggers. The task in this study is to identify event triggers from the literature across multiple levels of biological organization. In order to achieve high performances, the machine learning based approaches, such as neural networks, must be trained on a dataset with plentiful annotations. However, annotations might be difficult to obtain on the multiple levels, and annotated resources have so far mainly focused on the relations and processes at the molecular level. In this work, we aim to apply transfer learning for multiple-level trigger recognition, in which a source dataset with sufficient annotations on the molecular level is utilized to improve performance on a target domain with insufficient annotations and more trigger types. Results We propose a generalized cross-domain neural network transfer learning architecture and approach, which can share as much knowledge as possible between the source and target domains, especially when their label sets overlap. In the experiments, MLEE corpus is used to train and test the proposed model to recognize the multiple-level triggers as a target dataset. Two different corpora having the varying degrees of overlapping labels with MLEE from the BioNLP’09 and BioNLP’11 Shared Tasks are used as source datasets, respectively. Regardless of the degree of overlap, our proposed approach achieves recognition improvement. Moreover, its performance exceeds previously reported results of other leading systems on the same MLEE corpus. Conclusions The proposed transfer learning method can further improve the performance compared with the traditional method, when the labels of the source and target datasets overlap. The most essential reason is that our approach has changed the way parameters are shared. The vertical sharing replaces the horizontal sharing, which brings more sharable parameters. Hence, these more shared parameters between networks improve the performance and generalization of the model on the target domain effectively.

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Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

Cited by 12 publications

References 12 publications

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

A review of Chinese named entity recognition

Multiple-level biomedical event trigger recognition with transfer learning

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