• Annual Conference on Human and Language Technology
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• 2015.10a
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• pp.90-95
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• 2015

기계적 학습을 위해서는 일반적으로 많은 양의 수동 주석데이터(Manually Labeled Data)가 요구된다. 원격지도(Distant Supervision)는 현실적으로 부족한 주석데이터(Labeled Data)를 대신해 자동적으로 주석데이터를 수집하여 학습하는 접근 방식으로 관계 추출(Relation Extracion) 문제에 널리 활용되고 있다. 이때 필연적으로 많은 노이즈(Noise)가 발생되는데, 적합성 검증(Relevance Verification)을 통해 수집된 학습데이터를 정제함으로써 노이즈로 인한 변동성을 줄이고 결과적으로 향상된 성능을 보여주는 관계 추출 방법을 제시한다.

• Journal of KIISE
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• v.42 no.12
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• pp.1568-1574
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• 2015

Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.