@inproceedings{dfa5da1be9a342c2b6a7f99c4ff43be1,
title = "A Neural Topic Model Based on Variational Auto-Encoder for Aspect Extraction from Opinion Texts",
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.",
keywords = "Aspect extraction, Neural topic model, VAE",
author = "Peng Cui and Yuanchao Liu and Binqquan Liu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019 ; Conference date: 09-10-2019 Through 14-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32233-5\_51",
language = "英语",
isbn = "9783030322328",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "660--671",
editor = "Jie Tang and Min-Yen Kan and Dongyan Zhao and Sujian Li and Hongying Zan",
booktitle = "Natural Language Processing and Chinese Computing - 8th CCF International Conference, NLPCC 2019, Proceedings",
address = "德国",
}