Open AccessShort text semantic feature extension and classification based on LDA
Author(s) -
Liutong Xu,
Xue Zhao,
Hai Huang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/715/1/012110
Subject(s) - interpretability , feature (linguistics) , extension (predicate logic) , semantic feature , artificial intelligence , computer science , inference , natural language processing , pattern recognition (psychology) , linguistics , philosophy , programming language
To solve the problem of feature sparseness of short texts, we studied the application of LDA Topic Model on feature extension and classification of short texts. Training LDA on external long texts related to short texts, and achieving the inference and extension of short texts’ topics based on LDA solves the feature sparseness of short texts and improves the accuracy of classification effectively. Latent semantic information in LDA can also effectively improve the interpretability of short texts.