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.
Original language | English |
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Title of host publication | TAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 245-251 |
Number of pages | 7 |
ISBN (Electronic) | 9781467396066 |
DOIs | |
Publication status | Published - Feb 12 2016 |
Externally published | Yes |
Event | Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015 - Tainan, Taiwan Duration: Nov 20 2015 → Nov 22 2015 |
Conference
Conference | Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015 |
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Country | Taiwan |
City | Tainan |
Period | 11/20/15 → 11/22/15 |
Keywords
- document representation
- neural network
- word embedding
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications