Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis

Autor:	Fu, Hao-Ming, Cheng, Pu-Jen
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Information Retrieval
Zdroj:	SIGIR 2019: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Pages 1105 to 1108
Druh dokumentu:	Working Paper
DOI:	10.1145/3331184.3331320
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.06210 Zobrazit plný text záznamu View this record from Arxiv