Topic Modeling for Short Texts via Word Embedding and Document Correlation

Topic Modeling for Short Texts via Word Embedding and Document Correlation

Topic modeling is a widely studied foundational and interesting problem in the text mining domains. Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents. However, due to the limited length of short text, data sparsity impedes the infer...

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Bibliographic Details
Main Authors: Feng Yi, Bo Jiang, Jianjun Wu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Topic model
short texts
word embedding
document correlation
non-negative matrix factorization
regularization
Online Access:https://ieeexplore.ieee.org/document/8993771/

Internet

https://ieeexplore.ieee.org/document/8993771/

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