Method of Feature Reduction in Short Text Classification Based on Feature Clustering
Source abstract
Abstract
One decisive problem of short text classification is the serious dimensional disaster when utilizing a statistics-based approach to construct vector spaces. Here, a feature reduction method is proposed that is based on two-stage feature clustering (TSFC), which is applied to short text classification. Features are semi-loosely clustered by combining spectral clustering with a graph traversal algorithm. Next, intra-cluster feature screening rules are designed to remove outlier feature words, which improves the effect of similar feature clusters. We classify short texts with corresponding similar feature clusters instead of original feature words. Similar feature clusters replace feature words, and the dimension of vector space is significantly reduced. Several classifiers are utilized to evaluate the effectiveness of this method. The results show that the method largely resolves the dimensional disaster and it can significantly improve the accuracy of short text classification.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Text Analysis Techniques · Topic Modeling · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Central South University, Changsha 410073, China
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
Funding
Natural Science Foundation of Hunan Province · National Natural Science Foundation of China · Postdoctoral Science Foundation of China
References (36)
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