Results 1 to 10 of about 448,180 (169)
Aspect term extraction via information-augmented neural network
Complex & Intelligent Systems, 2022 Aspect term extraction (ATE) aims at identifying the aspect terms that are expressed in a sentence. Recently, Seq2Seq learning has been employed in ATE and significantly improved performance.
Ning Liu, Bo Shen
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Aspect Term Extraction Based on MFE-CRF [PDF]
Information, 2018 This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP).
Yanmin Xiang, Hongye He, Jin Zheng
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Enhancing Aspect Term Extraction with Soft Prototypes [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on. Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level. However, since the aspect terms and context words usually exhibit long-tail distributions, these taggers often ...
Zhuang Chen, Tieyun Qian
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Progressive Self-Training with Discriminator for Aspect Term Extraction [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Aspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the lack of sufficient annotated data.
Qianlong Wang +4 more
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Aspect Term Extraction with History Attention and Selective Transformation [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews. We present a new framework for tackling ATE. It can exploit two useful clues, namely opinion summary and aspect detection history.
Li, Xin +4 more
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Aspect term extraction based on word embedding
Труды Института системного программирования РАН, 2018 There are many sites in the Internet that allow users to share their opinions and write reviews about all kinds of goods and services. These views may be useful not only for other users, but also for companies which want to track their own reputation and
D. O. Mashkin, E. V. Kotelnikov
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Cross-domain aspect term extraction incorporating character-level features
Computers and Electrical Engineering, 2023 Abstract The aspect term extraction task requires a large amount of labeled data, which will be costly in money and manpower. To reduce the dependence of the aspect term extraction task on labeled data, existing approaches mainly utilize the existence of similar syntactic information or semantic information in the source and target domains ...
WangDengXiong wang, LiWeiJiang Li
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Applied SciencesAspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE ...
Worapoj Suwanpipob +2 more
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A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction [PDF]
Neurocomputing, 2021 Aspect-based sentiment analysis (ABSA) task is a multi-grained task of natural language processing and consists of two subtasks: aspect term extraction (ATE) and aspect polarity classification (APC). Most of the existing work focuses on the subtask of aspect term polarity inferring and ignores the significance of aspect term extraction.
Yang, Heng +4 more
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Bridge-Based Active Domain Adaptation for Aspect Term Extraction [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 As a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this problem by associating aspect terms with pivot
Zhuang Chen, Tieyun Qian
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