Double embedding and bidirectional sentiment dependence detector for aspect sentiment triplet extraction

Dai, Dawei; Chen, Tao; Xia, Shuyin; Wang, Guoyin

doi:10.1016/j.knosys.2022.109506

Cited by 8 publications

(6 citation statements)

References 30 publications

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“…Triplet Extraction ASTE Pipeline [9]; Multitask [18], [19], [20], [21], [22]; Word-Pair [23], [24], [25], [26] (Staff, Courteous, Positive); (Staff, Great, Positive); (Food, Terrible, Negative) joint pair extraction subtask, and ASTE subtask. This section briefly reviews different methods for these subtasks, which are summarized in Table 1.…”

Section: Methods Example Outputsmentioning

confidence: 99%

“…Several studies have proposed multitask frameworks to jointly extract triplets [18], [19], [20], [21], [22]. Zhang et al [18] used a sequence tagging strategy to extract aspect and opinion terms and predicted sentiment polarities using a table filling method.…”

Section: Triplet Extractionmentioning

confidence: 99%

“…Xu et al [20] designed a span-level model that can capture the span-to-span interactions instead of word-to-word interactions between the aspects and opinions for ASTE. Dai et al [21] presented a bidirectional sentiment-dependence detector with double embeddings to obtain better sentence representations and gather information from both the aspect-to-opinion and opinion-to-aspect directions. Zhang et al [22] proposed a dual decoder with a span copy mechanism that can extract multiple and overlapped triplets based on multitype information.…”

Section: Triplet Extractionmentioning

confidence: 99%

“…Unfortunately, the pipeline approach ignores the relationships between elements and is prone to error propagation. Alternatively, another viable option is to apply a multitask strategy to integrate both stages into a joint framework [18], [19], [20], [21], [22]. The main limitation of the joint approach is that it cannot efficiently handle scenarios in which a review contains multiple relational triplets that overlap with each other; e.g., in the previous example sentence, both opinion terms very courteous and great should be associated with the same aspect term, staff.…”

Section: Introductionmentioning

confidence: 99%

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Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Wang

et al. 2024

IEEE Trans. Affective Comput.

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Aspect-based sentiment triplet extraction (ASTE) aims to extract triplets consisting of aspect terms and their associated opinion terms and sentiment polarities from sentences, a relatively new and challenging subtask of aspect-based sentiment analysis (ABSA). Previous studies have used either pipeline models or unified tagging schema models. These models ignore the syntactic relationships between the aspect and its corresponding opinion words, which leads them to mistakenly focus on syntactically unrelated words. One feasible option is to use a graph convolution network (GCN) to exploit syntactic information by propagating the representation from the opinion words to the aspect. However, such a method considers all syntactic dependencies to be of the same type and thus may still incorrectly associate unrelated words to the target aspect through the iterations of graph convolutional propagation. Herein, a syntax-aware transformer (SA-Transformer) is proposed to extend the GCN strategy by fully exploiting the dependency types of edges to block inappropriate propagation. The proposed approach can obtain different representations and weights even for edges with the same dependency type according to their adjacent dependency type of edges. Instead of using a GCN layer, we used an L-layer SA transformer to encode syntactic information in the word-pair representation to improve performance. Experimental results on four benchmark datasets show that the proposed model outperforms various previous models for ASTE.

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Section: Methods Example Outputsmentioning

confidence: 99%

Section: Triplet Extractionmentioning

confidence: 99%

Section: Triplet Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Wang

et al. 2024

IEEE Trans. Affective Comput.

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“…Chen et al ( 2021 ) proposed a novel method which transforms ASTE task into a multi-turn machine reading comprehension task and propose a bidirectional MRC framework to address this challenge. Another end-to-end method (Dai et al, 2022 ) proposed a sentiment-dependence detector based on a dual-table structure that starts from two directions, aspect-to-opinion and opinion-to-aspect, to generate two sentiment-dependence tables dominated by two types of information. Shi et al ( 2022 ) proposed an interactive attention mechanism to jointly consider both the contextual features and the syntactic dependencies in an iterative interaction manner.…”

Section: Related Workmentioning

confidence: 99%

Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction

Zhang

2023

Front. Neurorobot.

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Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58.

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Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction

Ren,

Guo,

et al. 2024

J Intell Inf Syst

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Double embedding and bidirectional sentiment dependence detector for aspect sentiment triplet extraction

Cited by 8 publications

References 30 publications

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction

Span-based semantic syntactic dual enhancement for aspect sentiment triplet extraction

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