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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/112468

Title:	Distributed keyword vector representation for document categorization
Authors:	Hsieh, Yu Lun Liu, Shih Hung Chang, Yung Chun Hsu, Wen-Lian
Contributors:	資科系
Keywords:	Artificial intelligence;Neural networks;Vectors;Comprehensive performance evaluation;Context information;Document categorization;Document Representation;Information explosion;Similarity measure;Vector representations;word embedding;Vector spaces
Date:	2016-02
Issue Date:	2017-08-31 14:51:47 (UTC+8)
Abstract:	In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, for document categorization using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. Using a Chinese news corpus containing over 100,000 articles and five topics, we provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively categorize a document into the predefined topics. Results demonstrate that our method can achieve the best performances compared to several other approaches.
Relation:	TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence , 245-251
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/TAAI.2015.7407126
DOI:	10.1109/TAAI.2015.7407126
Appears in Collections:	[Department of Computer Science ] Proceedings

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