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首页> 外文期刊>Neurocomputing >Multi-task learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification
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Multi-task learning model based on recurrent convolutional neural networks for citation sentiment and purpose classification

机译：基于递归卷积神经网络的引文情感和目的分类多任务学习模型

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摘要

Automated citation analysis is a method of identifying sentiment and purpose of citations in the citing works. Most of the existing approaches use machine learning techniques to boost the performance of citation sentiment classification (CSC) and citation purpose classification (CPC), which are the main tasks of automated citation analysis. However, such approaches address CPC and CSC by learning them separately, which often suffer from inadequate training data and time-consuming for feature engineering. To alleviate these problems, we propose a multitask learning model based on convolutional and recurrent neural networks. The proposed model benefits from jointly learning CSC and CPC by modeling the citation context with task-specific information and shared layers for citation sentiment and purpose classification. The network architecture of the proposed model is useful to represent the citation context and extracts the features automatically. We conduct experiments on two public datasets to evaluate the performance of the proposed model using standard metrics such as precision, recall, and F-score. The results of CSC and CPC tasks show improvements relative to classical machine learning algorithms such as SVM and NB as well as single-task deep learning models. (C) 2019 Elsevier B.V. All rights reserved.
机译：自动化引文分析是一种在引用工作中识别引文的情感和目的的方法。现有的大多数方法都使用机器学习技术来提高引文情感分类（CSC）和引文目的分类（CPC）的性能，这是自动引文分析的主要任务。但是，此类方法通过分别学习CPC和CSC来解决，这常常遭受训练数据不足和特征工程耗时的困扰。为了缓解这些问题，我们提出了一种基于卷积神经网络和递归神经网络的多任务学习模型。拟议的模型受益于CSC和CPC的共同学习，方法是使用任务特定的信息和共享层对引用情绪和目的分类进行建模，对引用上下文进行建模。所提出模型的网络体系结构可用于表示引文上下文并自动提取特征。我们在两个公共数据集上进行实验，以使用诸如精度，召回率和F得分等标准指标来评估所提出模型的性能。 CSC和CPC任务的结果表明，相对于经典的机器学习算法（例如SVM和NB）以及单任务深度学习模型而言，这些改进。 （C）2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Neurocomputing》 |2019年第28期|195-205|共11页
  • 作者

    Yousif Abdallah; Niu Zhendong; Chambua James; Khan Zahid Younas;

  • 作者单位

    Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China;

    Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China|Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA USA;

    Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China;

    Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Citation sentiment; Convolutional; Citation purpose; Multitask learning;

    机译：引用情绪;卷积;引用目的;多任务学习;

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