Browsing by Author "Yousif, Abdallah"
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Item Citation Classification Using Multitask Convolutional Neural Network Model(Springer, 2018) Yousif, Abdallah; Niu, Zhendong; Nyamawe, Ally S.In the recent years, there has been an increased availability of scientific publications across the world connected through citations. To help analyze this huge amount of information, citation classification has been introduced to identify the opinions and purposes of the authors for citing earlier works. Existing approaches utilize machine learning techniques and report promising results in identifying the sentiment and purpose of the citations. However, most of the previous approaches tackle the citation sentiments and purposes classification in isolation. Moreover, they suffer from limited training data and time-consuming feature engineering process. In this paper, we address these issues by building a multitask learning model based on convolutional neural network. The proposed model jointly learns the citation sentiment classification (primary task) with the citation purpose classification as a related task to boost the classification performance. Experimental results on two public datasets show that our model outperforms the previous baseline methods and prove the effectiveness of multitask learning technique.Item Improving citation sentiment and purpose classification using hybrid deep neural network model(Springer, 2018) Yousif, Abdallah; Niu, Zhendong; Nyamawe, Ally S.; Hu, YatingAutomated citation classification has received much attention in recent years from the research community. It has many benefits in the bibliometric field such as improving the methods of measuring publications’ quality and productivity of the researchers. Most of the existing approaches are based on supervised learning techniques with discrete manual features to capture linguistic cues. Though these approaches have reported good results, extracting such features are time-consuming and may fail to encode the semantic meaning of the citation sentences, which consequently limits the classification performance. In this paper, a hybrid neural model is proposed, which combines convolutional and recurrent neural networks to capture local n-gram features and long-term dependencies of the text. The proposed model extracts the features automatically and classifies the sentiments and purposes of scientific citations. We conduct experiments using two publicly available datasets and the results show that our model outperforms previously reported results in terms of precision, recall, and F-score for citation classification.