A span-sharing joint extraction framework for harvesting aspect sentiment triplets

Li, You; Lin, Yongdong; Lin, Yuming; Chang, Liang; Zhang, Huibing

doi:10.1016/j.knosys.2022.108366

Cited by 31 publications

(9 citation statements)

References 33 publications

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“…By considering the interaction between the entire interval span of the aspect and opinion phrases, the model first completes the aspect term extraction and opinion term extraction. By enumerating all possible candidate phrase spans and sharing them by aspect terms and opinion terms, Li et al [27] proposed a span-sharing joint extraction framework that would handle the problem between multi-word entities as well as the problem between aspect terms and opinion terms. Using all possible phrase spans as input, Chen et al [28] proposed a bidirectional network for ASTE that extracts triples from candidate phrase spans in both directions.…”

Section: End-to-end Methodsmentioning

confidence: 99%

Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction

Wang,

Zheng,

Tang

et al. 2024

Preprint

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Aspect Sentiment Triplet Extraction is a task that extracts aspects, opinion words corresponding to aspects and sentiment polarity from review sentences. Although aspect emotion triple extraction technology has achieved remarkable results in recent years, it still has a few drawbacks. Many existing research methods ignore important issues, such as the dependency relationships between emotional triad elements and the detection of aspect and viewpoint boundary terms at the phrase-level. Due to the above problems, this paper proposes a multi-task joint learning architecture based on dual graph networks (DGNMT). Firstly, a shared sentence encoder module is proposed in the model. The middle-layer and top-level feature representations of the BERT encoder are applied to two different subtasks in this module. Secondly, Dual-Graph Network Learning module has been designed. As part of the dual-graph network learning layer, this module introduces a graph attention network based on phrase structure trees in the joint extraction task branch. For the triple extraction task branch, a graph convolution network is used based on the modified syntactic dependency graph in order to determine the syntactic relationships between triple elements. Additionally , the Multi-Task Tagging module is proposed to extract labels for tasks as well as triplet extraction tasks. And then, predictions are made based on 1D sequence tags and 2D word pair matrices, and the tag information of the two tasks is transformed into each other through a conversion strategy. According to the final experimental results, the model outperforms a state-of-the-art baseline model on four commonly used evaluation indicators.

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Section: End-to-end Methodsmentioning

confidence: 99%

Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction

Wang,

Zheng,

Tang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…A tagging-free approach (Mukherjee et al, 2021 ) is proposed to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair. Li et al ( 2022 ) proposed a span-sharing joint extraction framework to extract aspect terms and their corresponding opinion terms simultaneously in the last step, thereby avoiding error propagation. Hu et al ( 2023 ) used a span GCN for syntactic constituency parsing tree and a relational GCN (R-GCN) for commonsense knowledge graph to build an end-to-end model for the ASTE task.…”

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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“…The use of span representation with width representation and span pair representation with distance representation which proposed by Xu et al (2021) might give better representation and leads to better performance. A different approach might also leads to better performance in opinion triplet extraction, for example using Graph Neural Network (GNN) (Chen et al, 2021), using a generative text to text model (Zhang et al, 2021), decomposing triplet extraction into target tagging, opinion tagging and sentiment tagging (Chen et al, 2022), or uses span-sharing joint extraction (Li et al, 2022) . The importance of consistency in the data annotation process also needs to be emphasized, such as the various aspects that needs to be marked, ACKNOWLEDGMENT…”

Section: Evaluation and Analysismentioning

confidence: 99%

Opinion Triplet Extraction for Aspect-Based Sentiment Analysis Using Co-Extraction Approach

Genadi

Khodra

2022

Journal of Information and Communication Technology

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In aspect-based sentiment analysis, tasks are diverse and consist of aspect term extraction, aspect categorization, opinion term extraction, sentiment polarity classification, and relation extractions of aspect and opinion terms. These tasks are generally carried out sequentially using more than one model. However, this approach is inefficient and likely to reduce the model’s performance due to cumulative errors in previous processes. The co-extraction approach with Dual crOss-sharEd RNN (DOER) and span-based multitask acquired better performance than the pipelined approaches in English review data. Therefore, this research focuses on adapting the co-extraction approach where the extraction of aspect terms, opinion terms, and sentiment polarity are conducted simultaneously from review texts. The co-extraction approach was adapted by modifying the original frameworks to perform unhandled subtask to get the opinion triplet. Furthermore, the output layer on these frameworks was modified and trained using a collection of Indonesian-language hotel reviews. The adaptation was conducted by testing the output layer topology for aspect and opinion term extraction as well as variations in the type of recurrent neural network cells and model hyperparameters used, and then analysing the results to obtain a conclusion. The two proposed frameworks were able to carry out opinion triplet extraction and achieve decent performance. The DOER framework achieves better performance than the baselines on aspect and opinion term extraction tasks.

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A span-sharing joint extraction framework for harvesting aspect sentiment triplets

Cited by 31 publications

References 33 publications

Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction

Multi-task Dual-Graph Network framework for Aspect Sentiment Triplet Extraction

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

Opinion Triplet Extraction for Aspect-Based Sentiment Analysis Using Co-Extraction Approach

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