인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.4
- 수록면
- 123 - 133 (11page)
- DOI
- 10.7232/JKIIE.2020.46.2.123
이용수
초록· 키워드
Modeling sentence similarity plays an important role in natural language processing tasks such as question answering and plagiarism detection. Measuring semantic relationship of two sentences is challenging because of the variability and ambiguity of linguistic expression. Previous studies on sentence similarity are focusing on the configuration of input data and classification model structure. However, we focus on the sentence understanding process of human. Human brain stimulates association effect when one tries to understand a sentence describing landscape or object. The association effect that transforms text into image makes human robust to expression changes and word order changes in a sentence. To implement the association effect, we propose a new sentence similarity model based on Siamese network and Text2image generative adversarial network (GAN). The role of Siamese network is to compute the similarity between two sentences with the shared network weights. Inside the Siamese network, two subnetworks are composed of Text2image GAN which transforms the text data into image data. Once the sentences are transformed into image, latent features are extracted through VGGNet. The sentence similarity is computed from the normalized distance between two feature vectors. To verify our proposed method, we modify the MSCOCO dataset and experimental results show that the proposed method outperforms the benchmarked models without association process.
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목차
- 1. 서론
- 2. 관련연구
- 3. 방법론
- 4. 실험설계
- 5. 실험결과
- 6. 결론
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2020-530-000534428
