Data & code.

<div><p>Text embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose. However, the standard NMF approach, fundamentally based on the bag-of-words model, fails to utilize...

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Bibliographic Details
Main Author:	Mingming Li (502877) (author)
Other Authors:	Xingjie Wang (4796859) (author), Chunhua Li (18618) (author), Anping Zeng (9524345) (author)
Published:	2024
Subjects:	Sociology Science Policy Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified processing text embeddings natural language processing enhances semantic representation among various approaches nonnegative matrix factorization leverage semantic information model &# 8217 standard nmf approach conventional nmf models wasserstein metric used regularization term based wasserstein metric contextual information context matrix words model fundamentally based based regularization regularized nmf ordinary nmf novel nmf nmf framework wr model typically symmetric topic modeling spd ), significant loss results indicate proposed algorithms positive definite popular datasets numerical algorithms nlp ). may result manifold structure introduce modifications gradient computation findings suggest explicit expression effective method document clustering crucial role conduct experiments build upon also underscores
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Data & code.

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