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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מידע ביבליוגרפי

Principais autores:	Lars Hillebrand, David Biesner, Christian Bauckhage, Rafet Sifa
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	MDPI AG 2021-01-01
סדרה:	Machine Learning and Knowledge Extraction
נושאים:	matrix factorization tensor factorization word embeddings topic modeling NLP
גישה מקוונת:	https://www.mdpi.com/2504-4990/3/1/7
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!