Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification

Ma, Nianzu; Mazumder, Sahisnu; Wang, Hao; Liu, Bing

doi:10.18653/v1/2020.acl-main.512

Cited by 25 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ground truth is obtained by comparing the entity that appears earlier (e 1 ) in the sentence with the one that appears later (e 2 ). The dataset is split by convention (Panchenko et al, 2019;Ma et al, 2020) 1.…”

Section: Experimental Settingsmentioning

confidence: 99%

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

Li¹,

Qin²,

Liu³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other. Highquality CPC models can significantly benefit applications such as comparative question answering and review-based recommendation. Among the existing approaches, nondeep learning methods suffer from inferior performances. The state-of-the-art graph neural network-based ED-GAT (Ma et al., 2020) only considers syntactic information while ignoring the critical semantic relations and the sentiments to the compared entities. We propose Sentiment Analysis Enhanced COmparative Network (SAECON) which improves CPC accuracy with a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset present a significant improvement on the F1 scores over the best existing CPC approaches.

show abstract

Section: Experimental Settingsmentioning

confidence: 99%

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

Li¹,

Qin²,

Liu³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

show abstract

“…In recent years, Panchenko et al (2019) proposed a Comparative Preference Classification (CPC) task, to predict the preference (Better, Worse, None) between two annotated entities. Ma et al (2020) further proposed a Graph Attention Network for this task. However, CPC requires to annotate two compared entities in advance, which greatly limits its application in real scenes.…”

Section: Related Workmentioning

confidence: 99%

“…Some recent studies (Panchenko et al, 2019;Ma et al, 2020) proposed a new task named Comparative Preference Classification (CPC), to identify the explicit comparative preferences (e.g., Better, Worse, None) between the subject entity and the object entity. However, the CPC task requires that the subject and object entities have been annotated, which largely hinders its applications in real scenarios.…”

Section: Introductionmentioning

confidence: 99%

Comparative Opinion Quintuple Extraction from Product Reviews

Liu¹,

Xia²,

Yu³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, which ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these issues, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new multi-stage neural network approach which significantly outperforms the baseline systems extended from previous comparative opinion mining methods. The datasets and source code are publicly released at https://github. com/NUSTM/COQE.

show abstract

“…Syntactic Relation Previous RC models usually use the relative position information to identify which words are the entities in a sentence, e.g., Zeng et al (2015b). In addition, the syntax information of the sentences is proved useful in many natural language processing tasks (Faleńska and Kuhn, 2019;Ma et al, 2020;Chen et al, 2017a). Inspired by Yang et al (2016b), which adopt the dependency parse tree for RC (Ma et al, 2020), we also introduce the dependency relation as another type of position to emphasize the specific entities, and propose a novel application of the syntax positions.…”

Section: Related Workmentioning

confidence: 99%

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training

Wang¹,

Bao²,

Liu³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

View full text Add to dashboard Cite

This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high cooccurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two mechanisms to learn to decouple these easily-confused relations. On the one hand, an Entity-Guided Attention (EGA) mechanism, which leverages the syntactic relations and relative positions between each word and the specified entity pair, is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations by playing a pushing-away game between classifying a sentence into a true relation and its confusing relation. Extensive experiments are conducted on the FewRel dataset, and the results show that our proposed model achieves comparable and even much better results to strong baselines in terms of accuracy. Furthermore, the ablation test and case study verify the effectiveness of our proposed EGA and CAT, especially in addressing the relation confusion problem.

show abstract

Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification

Cited by 25 publications

References 24 publications

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer

Comparative Opinion Quintuple Extraction from Product Reviews

Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training

Contact Info

Product

Resources

About