한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제25권4호
  • /
  • Pages.141-148
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  • 2020
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Evaluation of English Term Extraction based on Inner/Outer Term Statistics

  • Kang, In-Su (Dept. of Computer Science, Kyungsung University)
  • 투고 : 2020.03.09
  • 심사 : 2020.04.20
  • 발행 : 2020.04.29
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⟨ 이전 논문 다음 논문 ⟩

초록

용어추출은 도메인 텍스트 모음으로부터 도메인 용어 목록을 인식하는 작업이다. 용어추출의 기존 효과적인 방법들은 비교사 방식으로 동작하며, 후보 용어 집합을 추출하는 작업과 후보 용어에 용어중요도를 할당하는 작업을 주요 단계로 포함한다. 후보 용어의 용어중요도 계산과 관련하여 본 논문에서는 후보 용어의 내부 및 외부용어집합을 활용한다. 내부용어집합은 후보 용어에 포함된 다른 짧은 용어들의 집합이며, 외부용어집합은 후보 용어가 포함된 다른 긴 용어들의 집합이다. 본 논문에서는 후보 용어의 내부 혹은 외부용어집합으로부터 후보 용어의 용어 강도를 계산하는 다양한 강도 함수들을 제시하고, 이들 용어 강도 값들과 C-value 점수를 결합하는 용어중요도 계산 방법을 소개한다. 생물학 및 전산언어학 분야 영어 데이터셋을 사용한 성능 평가에서는 제안된 방법의 용어추출 성능을 비교하고 분석한다. 제안된 방법은 생물학 및 전산언어학 분야 데이터셋에 대해 각각 최대 1%와 3% 차이의 성능 향상을 보였다.

Automatic term extraction is to recognize domain-specific terms given a collection of domain-specific text. Previous term extraction methods operate effectively in unsupervised manners which include extracting candidate terms, and assigning importance scores to candidate terms. Regarding the calculation of term importance scores, the study focuses on utilizing sets of inner and outer terms of a candidate term. For a candidate term, its inner terms are shorter terms which belong to the candidate term as components, and its outer terms are longer terms which include the candidate term as their component. This work presents various functions that compute, for a candidate term, term strength from either set of its inner or outer terms. In addition, a scoring method of a term importance is devised based on C-value score and the term strength values obtained from the sets of inner and outer terms. Experimental evaluations using GENIA and ACL RD-TEC 2.0 datasets compare and analyze the effectiveness of the proposed term extraction methods for English. The proposed method performed better than the baseline method by up to 1% and 3% respectively for GENIA and ACL datasets.

키워드

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