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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A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task
TL;DR: Wang et al. as mentioned in this paper introduced a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) task, which extracts aspect term, sentiment and opinion term triplets.
Journal ArticleDOI
ARTAN: Align reviews with topics in attention network for rating prediction
Yile Liang,Tieyun Qian,Huilin Yu +2 more
TL;DR: A novel model in which ratings and topic-level review information are integrated into a deep neural framework to address the problems of data sparsity and lack of explicability in rating prediction.
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ECO v1: Towards Event-Centric Opinion Mining
TL;DR: This paper proposes and forms the task of event-centric opinion mining based on event-argument structure and expression categorizing theory, and benchmarks this task by constructing a pioneer corpus and designing a two-step benchmark framework.
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Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
TL;DR: Wang et al. as mentioned in this paper proposed a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE, which achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge.
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