• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2012-2030
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• 2021

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.

• Journal of Computing Science and Engineering
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• v.8 no.3
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• pp.137-148
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• 2014

Named entity classification of Wikipedia articles is a fundamental research area that can be used to automatically build large-scale corpora of named entity recognition or to support other entity processing, such as entity linking, as auxiliary tasks. This paper describes a method of classifying named entities in Chinese Wikipedia with fine-grained types. We considered multi-faceted information in Chinese Wikipedia to construct four feature sets, designed different feature selection methods for each feature, and fused different features with a vector space using different strategies. Experimental results show that the explored feature sets and their combination can effectively improve the performance of named entity classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.415-417
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• 2021

Since the words and words in Chinese sentences are continuous and the length of vocabulary is huge, Chinese NER(Named Entity Recognition) always based on character representation. In recent years, many Chinese research has been reconsidered how to integrate the word information into the Chinese NER model. However, the traditional sequence model has complex structure, the slow inference speed, and an additional dictionary information is needed, which is difficult to implement in the industry. The approach in this paper has the state of the art and parallelizable, which is integrated the char-word embeddings, so that the model learns word information. The proposed model is easy to implement, and outperforms traditional model in terms of speed and efficiency, which is improved f1-score on two dataset.

• Annual Conference on Human and Language Technology
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• 2004.10d
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• pp.90-95
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• 2004

"고유명사 식별"은 사전에 등록되어 있지 않은 고유명사를 찾아내고 분류하는 과정으로 주로 인명, 지명, 조직 명을 처리 대상으로 한다. 처리할 데이터는 점점 많아지고 고유명사는 수시로 생겨나기 때문에 고유명사 식별은 정보검색, 질의응답, 기계번역시스템의 핵심 기술 중의 하나로 부각되었다. 고유명사 식별에 있어 정확률과 더불어 식별속도와 식별모듈의 크기가 시스템의 성능에 미치는 문제도 쟁점이 되고 있다. 본 논문에서는 SVM과 자질선택을 결합한 다양한 실험을 통하여 중국어 고유명사의 식별 효율을 높이는 방법을 연구하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.934-936
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• 2004

본문에서는 최근 들어 각광을 받고 있는 패턴인식 방법론인 Support Vector Machine을 이용하여 중국어 개체명을 식별하는 방법을 제안하고자 한다. SVM(support vector machine)은 입력 자질이 많을 경우에도 안정적인 성능을 나타내고 보편적으로 적용할 수 있는 모델을 개발할 수 있는 장점이 있다. 실험에서 어휘. 품사, 의미부류 등 많은 수의 자질을 이용하였다. 실험결과는 본문에서 제안한 방법이 튜닝을 거치지 않아도 좋은 성능을 나타낼 수 있고, 수행 속도도 만족스럽다는 것을 보여주었다.