A Study on the Integration of Recognition Technology for Scientific Core Entities (과학기술 핵심개체 인식기술 통합에 관한 연구)

Yun-Soo Choi (최윤수); Chang-Hoo Jeong (정창후); Hyun Yang Cho (조현양)

doi:10.3743/KOSIM.2011.28.1.089

A Study on the Integration of Recognition Technology for Scientific Core Entities

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2011, 28(1), pp.89~104
DOI : 10.3743/KOSIM.2011.28.1.089
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 17, 2011
Accepted : March 10, 2011
Published : March 30, 2011

Yun-Soo Choi ¹, Chang-Hoo Jeong ¹, Hyun Yang Cho ²

¹한국과학기술정보연구원
²경기대학교

Accredited

ABSTRACT

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In order to extract these entities automatically from scientific documents at once, we developed a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer and terminology extractor.

KEYWORDS

information extraction, named entity recognition, terminology extraction

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* References for papers published after 2023 are currently being built.

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Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on the Integration of Recognition Technology for Scientific Core Entities

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on the Integration of Recognition Technology for Scientific Core Entities

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.