计算机科学
简单(哲学)
依赖关系(UML)
情绪分析
代表(政治)
对偶(语法数字)
任务(项目管理)
方案(数学)
信息抽取
期限(时间)
人工智能
跨度(工程)
数据挖掘
情报检索
模式识别(心理学)
自然语言处理
数学
政治学
量子力学
法学
管理
经济
土木工程
艺术
哲学
数学分析
工程类
文学类
物理
认识论
政治
作者
Dongxu Li,Zhihao Yang,Yuquan Lan,Yunqi Zhang,Hui Zhao,Gang Zhao
标识
DOI:10.1145/3539618.3592060
摘要
Aspect sentiment triplet extraction (ASTE) is a task which extracts aspect terms, opinion terms, and sentiment polarities as triplets from review sentences. Existing approaches have developed bidirectional structures for term interaction. Sentiment polarities are subsequently extracted from aspect-opinion pairs. These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. SimSTAR does not introduce any additional bidirectional mechanism. The segment tagging scheme is capable to indicate all possible cases of spans and reveals more information through negative labels. Dual extractors are employed to make the sentiment extraction independent of the term extraction. We evaluate our model on four ASTE datasets. The experimental results show that our simple method achieves state-of-the-art performance.
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