2019-10-15-zhang19b.md

title	crossref	abstract	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
An Attentive Memory Network Integrated with Aspect Dependency for Document-Level Multi-Aspect Sentiment Classification	acml19	Document-level multi-aspect sentiment classification is one of the foundational tasks in natural language processing (NLP) and neural network methods have achieved great success in reviews sentiment classification. Most of recent works ignore the relation between different aspects and do not take into account the contexting dependent importance of sentences and aspect keywords. In this paper, we propose an attentive memory network for document-level multi-aspect sentiment classification. Unlike recent proposed models which average word embeddings of aspect keywords to represent aspect and utilize hierarchical architectures to encode review documents, we adopt attention-based memory networks to construct aspect and sentence memories. The recurrent attention operation is employed to capture long-distance dependency across sentences and obtain aspect-aware document representations over aspect and sentence memories. Then, incorporating the neighboring aspects related information into the final aspect rating predictions by using multi-hop attention memory networks. Experimental results on two real-world datasets TripAdvisor and BeerAdvocate show that our model achieves state-of-the-art performance.	inproceedings	Proceedings of Machine Learning Research	zhang19b	0	An Attentive Memory Network Integrated with Aspect Dependency for Document-Level Multi-Aspect Sentiment Classification	425	440	425-440	425	false	Zhang, Qingxuan and Shi, Chongyang	given family Qingxuan Zhang given family Chongyang Shi	2019-10-15		PMLR	Proceedings of The Eleventh Asian Conference on Machine Learning	101	inproceedings	date-parts 2019 10 15	http://proceedings.mlr.press/v101/zhang19b/zhang19b.pdf