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Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
- Source :
- SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 1105 to 1108
- Publication Year :
- 2024
-
Abstract
- Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.<br />Comment: International ACM SIGIR Conference 2019
Details
- Database :
- arXiv
- Journal :
- SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 1105 to 1108
- Publication Type :
- Report
- Accession number :
- edsarx.2401.06210
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1145/3331184.3331320