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Indexed by:Journal Papers

Date of Publication:2020-01-01

Journal:IEEE ACCESS

Included Journals:SCIE

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.