《计算机应用研究》|Application Research of Computers

一种文本幽默对比的Siamese双向GRU注意力模型

Siamese bidirectional GRU attention model for humor text comparison

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作者 顾艳,夏鸿斌,刘渊
机构 1.江南大学 人工智能与计算机学院,江苏 无锡 214122;2.江苏省媒体设计与软件技术重点实验室,江苏 无锡 214122
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文章编号 1001-3695(2021)04-010-1017-05
DOI 10.19734/j.issn.1001-3695.2020.05.0118
摘要 传统的幽默计算任务依赖人工构造特征,容易造成丢失特征,且主要集中在幽默判断。而基于深度学习的Siamese双向GRU注意力模型是对成对的幽默文本进行对比,判断哪一条语句更具有幽默性。首先,利用文本处理器对文本进行词嵌入训练;其次,使用双向GRU模型来获取每个单词的注释;最后,在全连接层执行幽默比较任务。在Semeval-2017 Task6-#HashtagWars数据集上进行实验,采用accuracy作为评估指标。实验结果表明,该模型与其他相关模型在幽默文本对比上有较明显提升。
关键词 人工智能; 自然语言理解; 双向GRU; 注意力机制; Siamese架构
基金项目 国家科学支撑计划课题(2015BAH54F01)
国家自然科学基金资助项目(61672264)
本文URL http://www.arocmag.com/article/01-2021-04-010.html
英文标题 Siamese bidirectional GRU attention model for humor text comparison
作者英文名 Gu Yan, Xia Hongbin, Liu Yuan
机构英文名 1.School of Artificial Intelligence & Computer,Jiangnan University,Wuxi Jiangsu 214122,China;2.Jiangsu Key Laboratory of Media Design & Software Technology,Wuxi Jiangsu 214122,China
英文摘要 Traditional humor computing tasks rely on artificially constructed features, which are prone to loss of features, and mainly focus on humorous judgment. The Siamese bidirectional GRU attention model based on deep learning compared pairs of humorous texts to determine which sentence was more humorous. Firstly, this paper used the text processor to train the word embedding. Secondly, it used the bidirectional GRU model to obtain the annotation of each word. Finally, it performed the humorous comparison task at the fully connected layer. This paper conducted experiments on the Semeval-2017 Task6-#HashtagWars data set, and used accuracy as an evaluation indicator. The experimental results show that the model and other related models have a significant improvement in the comparison of humorous text.
英文关键词 artificial intelligence; natural language understanding; bidirectional GRU; attention mechanism; Siamese architecture
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收稿日期 2020/5/5
修回日期 2020/6/22
页码 1017-1021
中图分类号 TP391
文献标志码 A