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Polyseme-Aware Vector Representation for Text Classification
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DOI：10.1109/ACCESS.2020.3010981.png)
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Abstract
En 中文
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.
Keywords:
Task analysis
Semantics
Text categorization
Training
Computational modeling
Context modeling
Microsoft Windows
Polysemous words
context clustering algorithm
PAVRM-Context
PAVRM-Center
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