Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision

Dai, Hongliang; Song, Yangqiu

doi:10.18653/v1/p19-1520

Cited by 115 publications

(82 citation statements)

References 24 publications

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“…The latter mostly regards the task as a sequence labeling problem by applying CRF-based approaches. Another related task -target and opinion span co-extraction (Qiu et al, 2011;Liu et al, 2013Liu et al, , 2014Liu et al, , 2015Wang et al, 2017;Xu et al, 2018;Dai and Song, 2019) is also often regarded as a sequence labeling problem.…”

Section: Related Workmentioning

confidence: 99%

Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Xu¹,

Li²,

Lu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

152

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Aspect Sentiment Triplet Extraction (ASTE)is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel positionaware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness 1 .

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Section: Related Workmentioning

confidence: 99%

Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Xu¹,

Li²,

Lu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

152

View full text Add to dashboard Cite

show abstract

“…This task also benefits from the representation learning ability of neural networks and achieves good performances [ 12 , 13 ]. It is also flexible for deep learning methods to learn the correlation between opinions and targets [ 14 , 15 ], infuse syntactic knowledge [ 16 ], or even benefit from weakly annotated data [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Target‐Oriented Opinion Words Extraction with Multiview‐Trained Machine Reading Comprehension Model

Zhang

Guo

et al. 2021

Computational Intelligence and Neuroscience

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Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.

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“…(3) RENANTE+ (Dai and Song, 2019) is originally an aspect-opinion co-extraction system in a weakly-supervised manner. (4) CMLA+ ) is an aspect-opinion co-extraction system modelling the interaction between the aspects and opinions.…”

Section: Baselines and Variantsmentioning

confidence: 99%

A Multi-task Learning Framework for Opinion Triplet Extraction

Zhang

Song

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or coextracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while coextraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms and opinion terms, and simultaneously parses sentiment dependencies between them with a biaffine scorer. At inference phase, the extraction of triplets is facilitated by a triplet decoding method based on the above outputs. We evaluate the proposed framework on four SemEval benchmarks for ASBA. The results demonstrate that our approach significantly outperforms a range of strong baselines and state-ofthe-art approaches. 1

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Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision

Cited by 115 publications

References 24 publications

Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Position-Aware Tagging for Aspect Sentiment Triplet Extraction

Enhancement of Target‐Oriented Opinion Words Extraction with Multiview‐Trained Machine Reading Comprehension Model

A Multi-task Learning Framework for Opinion Triplet Extraction

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