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Title: Structured Neural Topic Models for Reviews
We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews. VALTA defines a user-item encoder that maps bag-of-words vectors for combined reviews associated with each paired user; item onto structured embeddings, which in turn define per-aspect topic weights. We model individual reviews in a structured manner by inferring an aspect assignment for each sentence in a given review, where the per-aspect topic weights obtained by the user-item encoder serve to define a mixture over topics, conditioned on the aspect. The result is an autoencoding neural topic model for reviews, which can be trained in a fully unsupervised manner to learn topics that are structured into aspects. Experimental evaluation on large number of datasets demonstrates that aspects are interpretable, yield higher coherence scores than non-structured autoencoding topic model variants,; can be utilized to perform aspect-based comparison; genre discovery.  more » « less
Award ID(s):
1835309
NSF-PAR ID:
10107753
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
89
ISSN:
2640-3498
Page Range / eLocation ID:
3429--3439
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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