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Deep Neural Topic Model Using Contrastive Learning
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대조학습을 사용한 딥러닝 기반 토픽 모델링

논문 기본 정보

Type
Proceeding
Author
Mingi Shin (한국과학기술원) Sungwon Han (한국과학기술원) Sungkyu Park (기초과학연구원) Changwook Jung (한국과학기술원) Meeyoung Cha (기초과학연구원)
Journal
Korean Institute of Information Scientists and Engineers 한국정보과학회 학술발표논문집 Korea Software Congress 2021
Published
2021.12
Pages
302 - 304 (3page)

Usage

cover
📌
Topic
📖
Background
🔬
Method
🏆
Result
Deep Neural Topic Model Using Contrastive Learning
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Abstract· Keywords

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토픽 모델링은 주어진 말뭉치를 토대로 각 문서에 주제 구성을 할당하고, 각 주제에 구성 단어를 할당하는 과정이다. 말뭉치의 내용 구성을 더욱 잘 파악하기 위하여 토픽 모델의 성능을 향상시키려는 많은 노력이 있었으나, 맥락을 고려한 단어 군집을 추출하는데 한계가 있다. 이 논문에서는 사전 훈련된 언어모델 BERT 와 대조학습을 적용한 새로운 토픽 모델을 제안 ... View All

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