Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM

Interpretable Topic Extraction and Word Embedding Learning Using Non-Negative Tensor DEDICOM

Unsupervised topic extraction is a vital step in automatically extracting concise contentual information from large text corpora. Existing topic extraction methods lack the capability of linking relations between these topics which would further help text understanding. Therefore we propose utilizin...

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Bibliographic Details
Main Authors: Lars Hillebrand, David Biesner, Christian Bauckhage, Rafet Sifa
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Machine Learning and Knowledge Extraction
Subjects:
matrix factorization
tensor factorization
word embeddings
topic modeling
NLP
Online Access:https://www.mdpi.com/2504-4990/3/1/7

Internet

https://www.mdpi.com/2504-4990/3/1/7

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