Data & code.

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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