Buch ; Online: Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis
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 ... ...
| 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. Comment: International ACM SIGIR Conference 2019 |
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| Schlagwörter | Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Information Retrieval |
| Thema/Rubrik (Code) | 006 ; 004 |
| Erscheinungsdatum | 2024-01-11 |
| Erscheinungsland | us |
| Dokumenttyp | Buch ; Online |
| Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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