TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features |
| 영문제목(English Title) |
Towards Effective Entity Extraction of Scientific Documents using Discriminative Linguistic Features |
| 저자(Author) |
Sangwon Hwang
Jang-Eui Hong
Young-Kwang Nam
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| 원문수록처(Citation) |
VOL 13 NO. 03 PP. 1639 ~ 1658 (2019. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.
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| 키워드(Keyword) |
Named entity recognition
entity extraction
data mining
data cleaning
sentence segmentation
information extraction
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| 파일첨부 |
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