Chinese named entity recognition: The state of the art

Liu, Pan; Guo, Yanming; Wang, Fenglei; Li, Guohui

doi:10.1016/j.neucom.2021.10.101

Cited by 110 publications

(52 citation statements)

References 39 publications

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“…In the future, a much more reliable glossary of names should be established by making use of the organizational registers and donation registers of local Chinese societies to enhance the recognition accuracy of named entities (Humbel et al , 2021; Xu et al , 2020). Furthermore, future research may focus on collecting more external information, such as character representations and cross-lingual information, and maintaining more and higher quality CNER data sets like ENER, to improve the model performance of CNER (Liu et al , 2022).…”

Section: Discussionmentioning

confidence: 99%

“…In text analysis of different languages, the research related to English NER (ENER) was developed early, but it is relatively less difficult to implement because only the characteristics of the words themselves need to be considered without the problem of word segmentation. In contrast, in the case of CNER, the inherent specificity of the text must first be analysed for further lexical analysis, which will present a higher technical challenge in comparison with ENER (Liu et al , 2022; Sun and Wang, 2010). Until recently, numerous digital humanities tools commonly applied NER to develop digital humanities tools to support digital humanities research.…”

Section: Literature Reviewmentioning

confidence: 99%

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Development and application of a digital humanities research platform for biographies of Malaysian personalities

Chen

Ling

Chang

et al. 2022

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Purpose Digital humanities research platform for biographies of Malaysia personalities (DHRP-BMP) was collaboratively developed by the Research Center for Chinese Cultural Subjectivity in Taiwan, the Federation of Heng Ann Association Malaysia, and the Malaysian Chinese Research Center of Universiti Malaya in this study. Using The Biographies of Malaysia Henghua Personalities as the main archival sources, DHRP-BMP adopted the Omeka S, which is a next-generation Web publishing platform for institutions interested in connecting digital cultural heritage collections with other resources online, as the basic development system of the platform, to develop the functions of close reading and distant reading both combined together as the foundation of its digital humanities tools. Design/methodology/approach The results of the first-stage development are introduced in this study, and a case study of qualitative analysis is provided to describe the research process by a humanist scholar who used DHRP-BMP to discover the character relationships and contexts hidden in The Biographies of Malaysia Henghua Personalities. Findings Close reading provided by DHRP-BMP was able to support humanities scholars on comprehending full text contents through a user-friendly reading interface while distant reading developed in DHRP-BMP could assist humanities scholars on interpreting texts from a rather macro perspective through text analysis, with the functions such as keyword search, geographic information and social networks analysis for humanities scholars to master on the character relationships and geographic distribution from personality biographies, thus accelerating their text interpretation efficiency and uncovering the hidden context. Originality/value At present, a digital humanities research platform with real-time characters’ relationships analysis tool that can automatically generate visualized character relationship graphs based on Chinese named entity recognition (CNER) and character relationship identification technologies to effectively assist humanities scholars in interpreting characters’ relationships for digital humanities research is still lacking so far. This study thus presents the DHRP-BMP that offers the key features that can automatically identify characters’ names and characters’ relationships from personality biographies and provide a user-friendly visualization interface of characters’ relationships for supporting digital humanities research, so that humanities scholars could more efficiently and accurately explore characters’ relationships from the analyzed texts to explore complicated characters’ relationships and find out useful research findings.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Development and application of a digital humanities research platform for biographies of Malaysian personalities

Chen

Ling

Chang

et al. 2022

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“…For dataset division, four datasets were involved in the experiment, namely KIWID, BOSON, ClueNER, and People's Daily. We obtained the public data according to Table 2 in study (Liu et al, 2022). This study randomly divided KIWID, BOSON, and ClueNER into training, validation, and test sets according to a ratio of 8:1:1, respectively [refer to Zhang et al (2021)].…”

Section: Dataset Divisionmentioning

confidence: 99%

Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach

et al. 2022

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Named Entity Recognition (NER) is a crucial step in mining information from massive agricultural texts, which is required in the construction of many knowledge-based agricultural support systems, such as agricultural technology question answering systems. The vital domain characteristics of Chinese agricultural text cause the Chinese NER (CNER) in kiwifruit diseases and pests to suffer from the insensitivity of common word segmentation tools to kiwifruit-related texts and the feature extraction capability of the sequence encoding layer being challenged. In order to alleviate the above problems, effectively mine information from kiwifruit-related texts to provide support for agricultural support systems such as agricultural question answering systems, this study constructed a novel Chinese agricultural NER (CANER) model KIWINER by statistics-based new word detection and two novel modules, AttSoftlexicon (Criss-cross attention-based Softlexicon) and PCAT (Parallel connection criss-cross attention), proposed in this paper. Specifically, new words were detected to improve the adaptability of word segmentation tools to kiwifruit-related texts, thereby constructing a kiwifruit lexicon. The AttSoftlexicon integrates word information into the model and makes full use of the word information with the help of Criss-cross attention network (CCNet). And the PCAT improves the feature extraction ability of sequence encoding layer through CCNet and parallel connection structure. The performance of KIWINER was evaluated on four datasets, namely KIWID (Self-annotated), Boson, ClueNER, and People’s Daily, which achieved optimal F1-scores of 88.94%, 85.13%, 80.52%, and 92.82%, respectively. Experimental results in many aspects illustrated that methods proposed in this paper can effectively improve the recognition effect of kiwifruit diseases and pests named entities, especially for diseases and pests with strong domain characteristics

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“…As the amount of data increases, the workload of rule extraction increases, the difficulty of maintaining rule consistency increases, and rule‐based and dictionary‐based methods cannot address the heterogeneity and complexity of text and the thus achieve high GNER performance (Qiu, Xie, Wu, Tao, et al., 2019; Santoso et al., 2021). Compared with rule‐based methods, the statistical learning methods can learn from large amount of annotating training datasets to guide the recognition and extraction NER (Liu et al., 2022; Molina‐Villegas et al., 2021; Peng et al., 2021). The popular deep learning methods for named entity recognition generally base on word embeddings, which are able to learn similar representations for semantically or functionally similar words (Fang et al., 2021; Santoso et al., 2021; Tian et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

Xie

Xu³

et al. 2022

Earth and Space Science

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Geological reports are frequently used by geologists involved in geological surveys and scientific research to record the results and outcomes of geological surveys. With such a rich data source, a substantial amount of knowledge has yet to be mined and analyzed. This paper focuses on automatically information extraction from geological reports, namely, geological named entity recognition. Geological named entity recognition has an important role in data mining, knowledge discovery and Knowledge graph construction. Existing general named entity recognition models/tools are limited in the domain of geoscience due to the various language irregularities associated with geological text, such as informal sentence structures, several domain‐geoscience words, large character lengths and multiple combinations of independent words. We present Bidirectional encoder representations from transformers (BERT)‐(Bidirectional gated recurrent unit network) BiGRU‐ (Conditional random field) CRF, which is a deep learning‐based geological named entity recognition model that is designed specifically with these linguistic irregularities in mind. Based on the pretrained language model, an integrated deep learning model incorporating BERT, BiGRU and CRF is constructed to obtain character vectors rich in semantic information through the BERT pretrained language model to alleviate for the lack of specificity of static word vectors (e.g., word2vec) and to improve the extraction capability of complex geological entities. We demonstrate our proposed model by applying it to four test datasets, including a geoscience NER data set from regional geological reports, and by comparing its performance with those of five baseline models.

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Chinese named entity recognition: The state of the art

Cited by 110 publications

References 39 publications

Development and application of a digital humanities research platform for biographies of Malaysian personalities

Development and application of a digital humanities research platform for biographies of Malaysian personalities

Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach

Chinese Named Entity Recognition in the Geoscience Domain Based on BERT

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