Long Text Segmentation by String Vector based KNN
- Authors
- Jo, Taeho
- Issue Date
- 2017
- Publisher
- IEEE
- Keywords
- Long Text Segmentation; Semantic Similarity Similarity; String Vector; String Vector based KNN
- Citation
- 2017 19TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATIONS TECHNOLOGY (ICACT) - OPENING NEW ERA OF SMART SOCIETY, pp.805 - 810
- Journal Title
- 2017 19TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATIONS TECHNOLOGY (ICACT) - OPENING NEW ERA OF SMART SOCIETY
- Start Page
- 805
- End Page
- 810
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/28171
- ISSN
- 1738-9445
- Abstract
- In this research, we propose the string vector based version of KNN as the approach to the text segmentation. The text segmentation may be interpreted into the text classification, and encoding texts into string vectors improved previously the text classification performance. In this research, we encode sentence pairs or paragraph pairs into string vectors, and apply the string vector based version of KNN to the classification task mapped from the text segmentation. As the benefits from this research, we expect the better performance, the more compact representation, and the more transparency by doing so. The goal of this research is to improve the performance of the text segmentation system.
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Collections - School of Games > Game Software Major > 1. Journal Articles
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