Open AccessProceedings Article
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms.
Wenya Wang,Sinno Jialin Pan,Daniel Dahlmeier,Xiaokui Xiao +3 more
- pp 3316-3322
TLDR
A novel deep learning model, named coupled multi-layer attentions, where each layer consists of a couple of attentions with tensor operators that are learned interactively to dually propagate information between aspect terms and opinion terms.Abstract:
The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multilayer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves stateof-the-art performances compared with several baselines.read more
Citations
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Proceedings ArticleDOI
Aspect Sentiment Triplet Extraction Using Reinforcement Learning
TL;DR: This paper proposed a hierarchical reinforcement learning (RL) framework for aspect sentiment triplet extraction, which takes into account the mutual interactions among the triplet's components while improving exploration and sample efficiency.
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ExtremeReader: An interactive explorer for customizable and explainable review summarization
Xiaolan Wang,Yoshihiko Suhara,Natalie Nuno,Yuliang Li,Jinfeng Li,Nofar Carmeli,Stefanos Angelidis,Eser Kandogann,Wang-Chiew Tan +8 more
TL;DR: This paper demonstrates a novel summarization system, ExtremeReader, that overcomes the limitations of existing summarization systems and generates abstractive summaries with an underlying structure that helps users understand, explore, and seek explanations to the generated summaries.
Proceedings ArticleDOI
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
TL;DR: Wang et al. as discussed by the authors proposed a Semantic and syntactic enhanced aspect sentiment triplet extraction model (S3E2) to exploit the syntactic and semantic relationships between the triplet elements and jointly extract them.
Proceedings ArticleDOI
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions
Hongjie Cai,Rui Xia,Jianfei Yu +2 more
TL;DR: Wang et al. as mentioned in this paper proposed the Aspect-category-opinion-sentiment (ACOS) Quadruple Extraction task, which aims to extract all aspect-category opinion quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions.
Journal ArticleDOI
Incorporating Explicit Syntactic Dependency for Aspect Level Sentiment Classification
TL;DR: This paper proposes a novel syntactic-dependency-based attention network (SDATT) to incorporate explicit syntactic dependency for aspect level sentiment classification and shows superior performance of the proposed model over state-of-the-art baselines.
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