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• W2894944861 abstract "Abstract Fine-grained opinion extraction has received increasing interests in the natural language processing community. It usually involves several subtasks. Recently, joint methods and neural models have been investigated by several studies, achieving promising performance by using graph-based models such as conditional random field. In this work, we propose a novel end-to-end neural model alternatively for joint opinion extraction, by using a transition-based framework. First, we exploit multi-layer bi-directional long short term memory (LSTM) networks to encode the input sentences, and then decode incrementally based on partial output results dominated by a transition system. We use global normalization and beam search for training and decoding. Experiments on a standard benchmark show that the proposed end-to-end model can achieve competitive results compared with the state-of-the-art neural models of opinion extraction." @default.
• W2894944861 created "2018-10-12" @default.
• W2894944861 creator A5004953265 @default.
• W2894944861 creator A5058557531 @default.
• W2894944861 creator A5086452435 @default.
• W2894944861 date "2019-02-01" @default.
• W2894944861 modified "2023-10-18" @default.
• W2894944861 title "End-to-end neural opinion extraction with a transition-based model" @default.
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• W2894944861 doi "https://doi.org/10.1016/j.is.2018.09.006" @default.
• W2894944861 hasPublicationYear "2019" @default.
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