Title
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification.
Abstract
Recently, implicit representation models, such as embedding or deep learning, have been successfully adopted to text classification task due to their outstanding performance. However, these approaches are limited to small-or moderate-scale text classification. Explicit representation models are often used in a large-scale text classification, like the Open Directory Project (ODP)-based text classification. However, the performance of these models is limited to the associated knowledge bases. In this paper, we incorporate word embeddings into the ODP-based large-scale classification. To this end, we first generate category vectors, which represent the semantics of ODP categories by jointly modeling word embeddings and the ODP-based text classification. We then propose a novel semantic similarity measure, which utilizes the category and word vectors obtained from the joint model. The evaluation results clearly show the efficacy of our methodology in large-scale text classification. The proposed scheme exhibits significant improvements of 10% and 28% in terms of macro-averaging F1-score and precision at k, respectively, over state-of-the-art techniques.
Year
DOI
Venue
2018
10.1007/978-3-319-93037-4_30
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2018, PT II
Keywords
DocType
Volume
Text classification,Word embeddings
Conference
10938
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
14
4
Name
Order
Citations
PageRank
Kangmin Kim1138.69
Aliyeva Dinara200.34
Byung-Ju Choi301.69
Sangkeun Lee4145.09