Extraction of Transitional Relations in Healthcare Processes from Chinese Medical Text based on Deep Learning

Ren, F.; Song, Yuan; Gao, Feng

doi:10.1145/3325730.3325771

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Therefore, the deep learning-based methods are more suitable for the task of Chinese NER. NER as an important sub-task in Chinese text information extraction [71][72][73]. It is also an important part in the field of Chinese Natural Language Processing [74][75][76].…”

Section: Challenges and Future Directions Of Chinese Nermentioning

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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Section: Challenges and Future Directions Of Chinese Nermentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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A method for extracting tumor events from clinical CT examination reports

Pan

Zhao

Chen

et al. 2023

Journal of Biomedical Informatics

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Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

Andrade

Otálvaro

Jaramillo

et al. 2023

Preprint

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Automatic term extraction is a branch of Natural Language Processing (NLP) used to automatically generate lexicographic materials, such as glossaries, vocabularies, and dictionaries. It allows the creation of standard bases for building unified theories and translations between languages. Scientific literature shows great interest in the construction of automatic term extractors and includes several approaches, tools, algorithms, and methods that can be used for their construction; however, the number of articles in specialized databases is vast, and literature reviews are not recent. This paper presents a systematic literature mapping of the existing material for developing automatic term extractors to provide an overview of approaches, tools, algorithms, and methods used to create them. For this purpose, scientific articles in the domain published between 2015 and 2022 are reviewed and categorized. The mapping results show that among the most used approaches are statistical, with 21.85%; linguistic, with 9.75%; and hybrid, with 68.29%. In addition, there are various computational tools for terminology extraction where authors use different methods for their construction and whose results are measured under the criteria of precision and recall. Finally, 113 documents were selected to answer the research questions and to demonstrate how automatic term extractors are constructed. This paper presents a global summary of primary studies as an essential tool to approach this type of computational system construction.

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Extraction of Transitional Relations in Healthcare Processes from Chinese Medical Text based on Deep Learning

Cited by 3 publications

References 4 publications

A review of Chinese named entity recognition

A review of Chinese named entity recognition

A method for extracting tumor events from clinical CT examination reports

Approaches, tools, algorithms, and methods for automatic term extraction: A systematic literature mapping

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