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Prominent Aspect Term Extraction in Aspect Based Sentiment Analysis
2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), 2018In recent years unstructured text has flooded on the web and today it is a trend to comments, give feedback, share experiences toward products, articles, social issues, multimedia web documents etc. Most of the online social, as well as, commercial platform have started to provide separate space for user reviews in the form of natural text.
Ganpat Singh Chauhan, Yogesh Kumar Meena
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Joint aspect terms extraction and aspect categories detection via multi-task learning
Expert Systems with Applications, 2021Abstract Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task.
Youcai Wei +5 more
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Multi-task learning for aspect term extraction and aspect sentiment classification
Neurocomputing, 2020Abstract Aspect sentiment classification has a dependency over the aspect term extraction. The majority of the existing studies tackle these two problems independently, i.e., while performing aspect sentiment classification, it is assumed that the aspect terms are pre-identified. However, such assumptions are neither practical nor appropriate.
Md Shad Akhtar, Tarun Garg, Asif Ekbal
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A span-based model for aspect terms extraction and aspect sentiment classification
Neural Computing and Applications, 2020Sentiment analysis is a field of natural language processing, which is used to identify and extract opinions and attitudes from text. Aspect-based sentiment analysis aims to extract aspect terms and predict sentiment categories of the opinion aspects. It includes two subtasks: aspect terms extraction and aspect sentiment classification.
Yanxia Lv +5 more
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Unsupervised Neural Aspect Extraction with Related Terms
2020The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is still a challenge to extract both an ...
Timur Sokhin +2 more
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STC: Stacked Two-stage Convolution for Aspect Term Extraction
2021 International Symposium on Electrical, Electronics and Information Engineering, 2021Aspect term extraction (ATE) aims to extract aspect terms from reviews as opinion targets for sentiment analysis. Although some of the previous works prove that dependency relationship between aspect terms and context is useful for ATE, they have barely tried to use graph neural networks to capture valuable information in dependency patterns ...
Ruiqi Wang +3 more
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Syntax-Aware Representation for Aspect Term Extraction
2019Aspect Term Extraction (ATE) plays an important role in aspect-based sentiment analysis. Syntax-based neural models that learn rich linguistic knowledge have proven their effectiveness on ATE. However, previous approaches mainly focus on modeling syntactic structure, neglecting rich interactions along dependency arcs. Besides, these methods highly rely
Jingyuan Zhang +4 more
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Aspect Based Sentiment Analysis in Bangla Dataset Based on Aspect Term Extraction
2020Recent years have seen rapid growth of research on sentiment analysis. In aspect-based sentiment analysis, the idea is to take sentiment analysis a step further and find out what exactly someone is talking about, and then measuring the sentiment if she or he likes or dislikes it.
Sabrina Haque +7 more
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DomSent: Domain-Specific Aspect Term Extraction in Aspect-Based Sentiment Analysis
2019In recent research aspect-based sentiment analysis has played a vital role in identifying user’s opinions from the unstructured natural text. One of the most critical subtasks in aspect-based sentiment analysis is to extract the most prominent aspect terms.
Ganpat Singh Chauhan, Yogesh Kumar Meena
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