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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Syntax-Aware Representation for Aspect Term Extraction
TL;DR: A syntax-directed attention network and a contextual gating mechanism to synthesize syntactic information with structure-free features are introduced and achieves state-of-the-art performance on three widely used benchmark datasets.
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Arabic Aspect Extraction Based on Stacked Contextualized Embedding With Deep Learning
TL;DR: This work aims to develop the Arabic AE task by proposing transfer learning using state-of-art pre-trained contextual language models, and develops the model by concatenating the Bidirectional Encoder Representation from Transformers language model and contextualize string embeddings as a stacked embedded layer for better word representation for Arabic language.
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A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis
TL;DR: A novel cascade social comment sentiment analysis model for jointly tackling the E2E-ABSA problem, namely CasNSA, which substantially outperforms state-of-the-art methods, even when using fixed words embedding rather than pre-trained BERT fine tuning.
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Sentiment Analysis of Arabic Tweets: Opinion Target Extraction
TL;DR: An opinion target extraction method from Arabic tweets is proposed, and it is shown that, with 500 tweets collected and manually tagged, SVM gives the highest precision and recall.
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