Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model

Gao, Kai; Xu, Hua; Wang, Jiushuo

doi:10.1007/978-3-319-18032-8_1

Cited by 53 publications

(16 citation statements)

References 7 publications

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“…Russo et al [20] introduced commonsense knowledge into the RB for emotion cause recognition. Gao et al [21], [22] did further research work around the rule-based approach. Since rules cannot cover all language phenomena, some researchers tried to apply machine learning approaches to ECE.…”

Section: A Ece and Ecpementioning

confidence: 99%

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Liu

et al. 2021

IEEE Access

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Section: A Ece and Ecpementioning

confidence: 99%

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Liu

et al. 2021

IEEE Access

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“…Emotion Cause Extraction Lee et al (2010a,b) first studied emotion cause extraction and designed a linguistic rule-based system to detect cause events. Early work attempted rule-based (Chen et al, 2010;Neviarouskaya and Aono, 2013;Gao et al, 2015), commonsense-based (Russo et al, 2011), and traditional machine learning based (Ghazi et al, 2015) approaches to extract causes for certain emotion expressions. Gui et al (2016) proposed an event-driven multikernel SVM method and released a benchmark corpus.…”

Section: Related Workmentioning

confidence: 99%

Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction

Wei¹,

Zhao²,

Mao³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

103

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Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. Previous work employs two-step approaches, in which the first step extracts emotion clauses and cause clauses separately, and the second step trains a classifier to filter out negative pairs. However, such pipeline-style system for emotion-cause pair extraction is suboptimal because it suffers from error propagation and the two steps may not adapt to each other well. In this paper, we tackle emotion-cause pair extraction from a ranking perspective, i.e., ranking clause pair candidates in a document, and propose a onestep neural approach which emphasizes interclause modeling to perform end-to-end extraction. It models the interrelations between the clauses in a document to learn clause representations with graph attention, and enhances clause pair representations with kernel-based relative position embedding for effective ranking. Experimental results show that our approach significantly outperforms the current two-step systems, especially in the condition of extracting multiple pairs in one document.

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“…For the following studies on ECE task, this corpus has become a benchmark dataset. While early studies mainly adopted the rule-based methods Gao et al, 2015;Gui et al, 2014) and machine learning methods (Ghazi et al, 2015) to deal with the ECE task, recent studies has begun to apply the deep learning methods to this task (Gui et al, 2017;Chen et al, 2018;Yu et al, 2019;Li et al, 2019;Fan et al, 2019;.…”

Section: Related Workmentioning

confidence: 99%

A Symmetric Local Search Network for Emotion-Cause Pair Extraction

Cheng¹,

Jiang²,

Yin³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which first extracted emotion clauses and cause clauses individually, then paired the emotion and cause clauses, and filtered out the pairs without causality. Different from this method that separated the detection and the matching of emotion and cause into two steps, we propose a Symmetric Local Search Network (SLSN) model to perform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the emotion subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs. Experimental results on the ECPE corpus demonstrate the superiority of our SLSN over existing state-of-the-art methods.

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Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model

Cited by 53 publications

References 7 publications

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction

A Symmetric Local Search Network for Emotion-Cause Pair Extraction

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