Indexed by:
Journal Papers
First Author:
Guo, Shun
Correspondence Author:
Yao, NM (corresponding author), Dalian Univ Technol, Dept Comp Sci & Technol, Dalian 116024, Peoples R China.
Co-author:
Yao, Nianmin
Date of Publication:
2020-01-01
Journal:
IEEE ACCESS
Included Journals:
SCIE
Document Type:
J
Volume:
8
Page Number:
135686-135699
ISSN No.:
2169-3536
Key Words:
Task analysis; Semantics; Text categorization; Training; Computational
modeling; Context modeling; Microsoft Windows; Polysemous words; context
clustering algorithm; PAVRM-Context; PAVRM-Center
Abstract:
Representation models for text classification have recently shown impressive performance. However, these models neglect the importance of polysemous words in text. When polysemous words appear in a text, imprecise polysemous word embeddings will produce low-quality text representation that results in changing the original meaning of the text. To address this problem, in this paper, we present a more effective model architecture, the polyseme-aware vector representation model (PAVRM), to generate more precise vector representations for words and texts. The PAVRM can effectively identify polysemous words in a corpus with a context clustering algorithm. Additionally, we propose two methods to construct polysemous word representations, PAVRM-Context and PAVRM-Center. Experiments conducted on three standard text classification tasks and a custom text classification task demonstrate that the proposed PAVRM can be effectively introduced into existing models to generate higher-quality word and text representations to achieve better classification performance.
Translation or Not:
no
