Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction

Wu, Zhen; Ying, Chengcan; Zhao, Fang; Fan, Zhifang; Dai, Xinyu; Xia, Rui

doi:10.18653/v1/2020.findings-emnlp.234

Cited by 135 publications

(159 citation statements)

References 28 publications

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“…generally outperforms the previous two end-to-end approaches on the four datasets. As mentioned in Table 3, it is challenging to make a fair comparison between the previous ABSA framework RACL (Chen and Qian, 2020), which also address the ATE and OTE tasks while solving other ABSA subtasks, and our approach as well as the GTS (Wu et al, 2020). This is because the mentioned approaches have different task settings.…”

Section: Additional Comparisonsmentioning

confidence: 94%

“…End-to-end The end-to-end methods aim to jointly extract full triplets in a single stage. Previous work by Zhang et al (2020) and Wu et al (2020) independently predict the sentiment relation for all possible word-word pairs, hence they require decoding heuristics to determine the overall sentiment polarity of a triplet. JET (Xu et al, 2020b) models the ASTE task as a structured prediction problem with a position-aware tagging scheme to capture the interaction of the three elements in a triplet.…”

Section: Baselinesmentioning

confidence: 99%

“…Test results on the ATE and OTE tasks with sub-optimal evaluation method. Our method and GTS(Wu et al, 2020) allow for ATE and OTE tasks to be predicted independently from the ASTE task. However, JET…”

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confidence: 99%

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Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Xu¹,

Chia²,

Bing³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Aspect Sentiment Triplet Extraction (ASTE)is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and opinion word. Thereby, they cannot perform well on targets and opinions which contain multiple words. Our proposed span-level approach explicitly considers the interaction between the whole spans of targets and opinions when predicting their sentiment relation. Thus, it can make predictions with the semantics of whole spans, ensuring better sentiment consistency. To ease the high computational cost caused by span enumeration, we propose a dual-channel span pruning strategy by incorporating supervision from the Aspect Term Extraction (ATE) and Opinion Term Extraction (OTE) tasks. This strategy not only improves computational efficiency but also distinguishes the opinion and target spans more properly. Our framework simultaneously achieves strong performance for the ASTE as well as ATE and OTE tasks. In particular, our analysis shows that our spanlevel approach achieves more significant improvements over the baselines on triplets with multi-word targets or opinions. 1 * Equal contribution. Lu Xu and Yew Ken Chia are under the Joint PhD Program between Alibaba and

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Section: Additional Comparisonsmentioning

confidence: 94%

Section: Baselinesmentioning

confidence: 99%

See 1 more Smart Citation

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Xu¹,

Chia²,

Bing³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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show abstract

“…Grid Tagging Schema. To tackle the ASTE task, a Grid Tagging Schema (GTS) was proposed by Wu et al (2020), which adopts six tags G = {A, O, NEG, NEU, POS, N} to represent the relationship for any pair of two words (w i , w j ) in a sentence. The two tags, A and O, denote the word-pair (w i , w j ) is the same aspect or opinion, respectively.…”

Section: Task Definition and Preliminariesmentioning

confidence: 99%

Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction

Chen¹,

Huang²,

Liu³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three elements and has the problem of error propagation. In this paper, we propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model (S 3 E 2 ) to fully exploit the syntactic and semantic relationships between the triplet elements and jointly extract them. Specifically, we design a Graph-Sequence duel representation and modeling paradigm for the task of ASTE: we represent the semantic and syntactic relationships between word pairs in a sentence by graph and encode it by Graph Neural Networks (GNNs), as well as modeling the original sentence by LSTM to preserve the sequential information. Under this setting, we further apply a more efficient inference strategy for the extraction of triplets. Extensive evaluations on four benchmark datasets show that S 3 E 2 significantly outperforms existing approaches, which proves our S 3 E 2 's superiority and flexibility in an end-to-end fashion.

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“…In ABSA, aspect (or called opinion target) extraction and opinion term extraction are two fundamental tasks. Aspect is the word or phrase in the reviews referring to the object towards which users show attitudes, while opinion terms are those words or phrases representing users' attitudes (Wu et al, 2020). For example, in the sentence "The dim sum is delicious.…”

Section: Introductionmentioning

confidence: 99%

Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction

Feng¹,

Rao²,

Tang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Targetoriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.

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Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction

Cited by 135 publications

References 28 publications

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction

Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction

Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction

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