Question-Answering Aspect Classification with Hierarchical Attention Network

Wu, Hanqian; Liu, Mumu; Wang, Jingjing; Xie, Jue; Shen, Chenlin

doi:10.1007/978-3-030-01716-3_19

Cited by 4 publications

(2 citation statements)

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“…Q&A-style reviews, as a novel form of online review, have great research value. We have achieved some research results in this paper and some previous work [31,32], but there are still many aspects to be studied and improved. Our future work would like to focus on the followings:…”

Section: Future Workmentioning

confidence: 77%

“…One of the barriers of these two tasks on Q&A-style reviews is the corpus about Q&A-style reviews-especially the Chinese corpus-is scarce. The good news is that we recently resolved this difficulty in previous work by designing a set of elaborate annotation rules and building a high-quality annotated corpus [31,32]. Beyond that, there are some other important problems needed to be addressed in ATE and ACC tasks.…”

Section: Introductionmentioning

confidence: 99%

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Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters

Cheng

Wang

et al. 2020

Cluster Comput

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Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

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Section: Future Workmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%