期刊
INFORMATION PROCESSING & MANAGEMENT
卷 58, 期 6, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2021.102729
关键词
Fine-grained opinion analysis; Aspect-based generation; Pair-wise generation; Joint model
资金
- National Natural Science Foundation of China [61772378]
- National Key Research and Development Program of China [2017YFC1200500]
- Research Foundation of Ministry of Education of China [18JZD015]
- Major Projects of the National Social Science Foundation of China [11ZD189]
This paper focuses on the extraction of opinion target-word pairs from user reviews, proposing a new task named Aspect-Based Pair-wise Opinion Generation (ABPOG) and developing a new model to address this issue. Experimental results demonstrate the effectiveness of the model on a Chinese automotive reviews dataset.
The extraction of opinion target-word pairs from user reviews has received much attention recently, since it can provide essential information for fine-grained opinion analysis. However, according to our statistics on a large-scale dataset of Chinese reviews, about 60% reviews do not explicitly show opinion targets or words. To investigate this problem, this paper introduces a new task under fine-grained opinion analysis, named Aspect -Based Pair-wise Opinion Generation (ABPOG), which aims to generate opinion target-word pairs based on reviews and aspects. To perform this task, we develop a sequence-to-sequence model for opinion target-word pair generation by extending the pointer-generator network with two approaches: (1) an aspect-aware encoder that receives an additional aspect embedding as input to extract aspect-specific features, (2) two hierarchical decoders including a token-level GRU and a global GRU to generate opinion targets and words jointly. To empirically evaluate our task and model, we develop a multi-aspect dataset for ABPOG based on Chinese automotive reviews. Extensive experiments on our dataset show that our model outperforms several strong baselines adapted from the state-of-the-art aspect-based summarization method.
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