A Survey on Named Entity Recognition

Yan, Wen; Fan, Cong; Chen, Geng; Chen, Xin; Chen, Ming

doi:10.1007/978-981-13-9409-6_218

Cited by 22 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, the annotated sentences can be complemented with structured data to further improve the data's machine readability. This can be done in an automated fashion by leveraging techniques such a Named Entity Recognition (NER) [39] to automatically detect concepts in a sentence. This results in additional structured data which in turn can be further enhanced by linking these concepts to other knowledge graphs, by means of Entity Linking [45].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing Scholarly Discourse Annotations

Oelen

Stocker

Auer

2021

26th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

The number of scholarly publications grows steadily every year and it becomes harder to find, assess and compare scholarly knowledge effectively. Scholarly knowledge graphs have the potential to address these challenges. However, creating such graphs remains a complex task. We propose a method to crowdsource structured scholarly knowledge from paper authors with a web-based user interface supported by artificial intelligence. The interface enables authors to select key sentences for annotation. It integrates multiple machine learning algorithms to assist authors during the annotation, including class recommendation and key sentence highlighting. We envision that the interface is integrated in paper submission processes for which we define three main task requirements: The task has to be (1) straightforward (2) time efficient (3) well-defined. We evaluated the interface with a user study in which participants were assigned the task to annotate one of their own articles. With the resulting data, we determined whether the participants were successfully able to perform the task. Furthermore, we evaluated the interface's usability and the participant's attitude towards the interface with a survey. The results suggest that sentence annotation is a feasible task for researchers and that they do not object to annotate their articles during the submission process. CCS CONCEPTS• Human-centered computing → Web-based interaction; • Information systems → Web interfaces; Crowdsourcing.

show abstract

Section: Discussionmentioning

confidence: 99%

“…These sentences must be transformed into more machine-readable descriptions. This can be done automatically using Named Entity Recognition and Classification (NERC) [39]. The resulting recognized entities can leverage the already determined discourse class.…”

Section: Data Consumptionmentioning

confidence: 99%

Crowdsourcing Scholarly Discourse Annotations

Oelen

Stocker

Auer

2021

26th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…The rule-based NER methods rely on a large number of language experts to formulate rule templates with punctuation marks, keywords, etc. [21]. With the development of technology, Wang et al [22] used a sublingual mechanism to propose a new Chinese name recognition method.…”

Section: Rule-based Recognition Methodsmentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

View full text Add to dashboard Cite

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.

show abstract

“…Based on experimental analysis, Wen et al [12] pointed out that compared with traditional models, the deep learning-based methods can better capture the context 2…”

Section: Related Workmentioning

confidence: 99%

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Deng

Chen

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the growing popularity of traditional Chinese medicine (TCM) in the world and the increasing awareness of intellectual property protection, the number of TCM patent application is growing year by year. TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short-Term Memory neural network with Conditional Random Field (BiLSTM-CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM-CRF-based method provides superior performance in comparison with various baseline methods.

show abstract

A Survey on Named Entity Recognition

Cited by 22 publications

References 13 publications

Crowdsourcing Scholarly Discourse Annotations

Crowdsourcing Scholarly Discourse Annotations

A review of Chinese named entity recognition

Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM‐CRF

Contact Info

Product

Resources

About