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2019 | OriginalPaper | Buchkapitel

A Neural Topic Model Based on Variational Auto-Encoder for Aspect Extraction from Opinion Texts

verfasst von : Peng Cui, Yuanchao Liu, Binqquan Liu

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Aspect extraction is an important task in ABSA (Aspect Based Sentiment Analysis). To address this task, in this paper we propose a novel variant of neural topic model based on Variational Auto-encoder (VAE), which consists of an aspect encoder, an auxiliary encoder and a hierarchical decoder. The difference from previous neural topic model based approaches is that our proposed model builds latent variable in multiple vector spaces and it is able to learn latent semantic representation in better granularity. Additionally, it also provides a direct and effective solution for unsupervised aspect extraction, thus it is beneficial for low-resource processing. Experimental evaluation conducted on both a Chinese corpus and an English corpus have demonstrated that our model has better capacity of text modeling, and substantially outperforms previous state-of-the-art unsupervised approaches for aspect extraction.

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Vorheriges Kapitel Implicit Objective Network for Emotion Detection

Nächstes Kapitel Using Dependency Information to Enhance Attention Mechanism for Aspect-Based Sentiment Analysis

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Titel: A Neural Topic Model Based on Variational Auto-Encoder for Aspect Extraction from Opinion Texts
verfasst von: Peng Cui
Yuanchao Liu
Binqquan Liu
Verlag: Springer International Publishing
Buch: Natural Language Processing and Chinese Computing
Print ISBN: 978-3-030-32232-8

Electronic ISBN: 978-3-030-32233-5

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-32233-5_51

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