基于特征融合和集成学习的建议语句分类模型

doi:10.6040/j.issn.1672-3961.0.2018.207

摘要/Abstract

摘要：

建议挖掘作为一项新兴研究任务近年来逐渐受到了研究者的关注。与英文相比,中文的建议表达形式更为丰富,呈现出许多不同特点,因此有必要在中文环境下开展建议挖掘研究。针对建议挖掘中的建议语句检测这一核心任务,提出一种综合应用Stacking和Bagging方法的集成学习模型来进行建议语句分类。使用Stacking组合分类器来构建概率特征空间,分别使用卷积神经网络(convolutional neural network, CNN)和段落向量模型(paragraph vector, PV)构建评论文本的CNN特征空间和段落向量特征空间,对上述特征进行融合,并训练Bagging分类器来对建议语句分类。在中文数据集上的试验结果验证了本研究模型的有效性。

关键词: 建议挖掘, 建议语句分类, 卷积神经网络, 集成学习, 特征融合

Abstract:

As an emerging research task, suggestion mining has gradually attracted attention of researchers in recent years. Compared with English language suggestion expression forms, those of Chinese were more abundant, and many different characteristics were present. It was necessary to carry out the research on suggestion mining in the Chinese environment. As suggestion sentence detection was the core task of suggestion mining, this research proposed an ensemble learning model that integrated the Stacking and Bagging methods to classify the reviews for the detection of suggestion sentence. The model firstly used Stacking to combine classifiers and constructed probabilistic feature space. Then, the convolution neural network (CNN) and paragraph vector (PV) model were used to construct the CNN feature space and paragraph vector feature space of the reviews respectively. Finally, the above features were fused and the Bagging classifier was trained to classify suggestion sentences. Experimental results on Chinese dataset verified the effectiveness of the model.

Key words: suggestion mining, suggestion sentence classification, convolutional neural network, ensemble learning, feature fusion

中图分类号:

TP391.1

张璞,刘畅,王永. 基于特征融合和集成学习的建议语句分类模型[J]. 山东大学学报 (工学版), 2018, 48(5): 47-54.

Pu ZHANG,Chang LIU,Yong WANG. Suggestion sentence classification model based on feature fusion and ensemble learning[J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 47-54.

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模型	精确率	召回率	F值	准确率
NB	83.42	81.11	82.25	82.17
FM	87.03	81.17	84.00	84.23
LR	88.58	82.05	85.19	85.20
RF	87.36	82.55	84.89	85.01
ET	86.71	83.02	84.82	84.87
TextCNN	88.14	72.10	79.32	79.70
Pos-TextCNN	84.37	79.62	81.93	81.49
Stacking+Bagging	87.48	85.20	86.32	86.25
Stacking+Bagging+CNN	87.92	84.90	86.38	86.47
Stacking+Bagging+PV	88.27	85.14	86.68	86.66
CNN+PV+Bagging	86.86	82.97	84.87	84.87
Stacking+Bagging+CNN+PV	88.63	86.06	87.33	87.28

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