Browsing by Author "Bakhti, Khadidja"

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    A new scheme for citation classification based on convolutional neural networks
    (KSI Research Inc, 2018) Bakhti, Khadidja; Niu, Zhendong; Nyamawe, Ally S.
    Automated classification of citation function in scientific text is a new emerging research topic inspired by traditional citation analysis in applied linguistic and scientometric fields. The aim is to classify citations in scholarly publication in order to identify author’s purpose or motivation for quoting or citing a particular paper. Several citation schemes have been proposed to classify the citations into different functions. However, it is extremely challenging to find standard scheme to classify citations, and some of the proposed schemes have similar functions. Moreover, most of previous studies mainly used classical machine learning methods such as support vector machine and neural networks with a number of manually created features. These features are incomplete and suffer from time-consuming and error prone weakness. To address these problems, we present a new citation scheme with less functions and propose a deep learning model for classification. The citation sentences and author’s information were fed to convolutional neural networks to build citation and author representations. A corpus was built using the proposed scheme and a number of experiments were carried out to assess the model. Experimental results have shown that the proposed approach outperforms the existing methods in term of accuracy, precision and recall
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    Semi-automatic annotation for citation function classification
    (Institute of Electrical and Electronics Engineers ( IEEE), 2018) Bakhti, Khadidja; Niu, Zhendong; Nyamawe, Ally S.
    Citation function classification generally is a way to classify citations into different functions. Commonly, functions are used to determine authors purposes of citing a particular paper. Automated classification of citation functions plays a significant role in increasing educational use of citation function in scholarly publication. Due to varied informative citation, many researchers are experiencing difficulties in retrieving automatically the nature of the citations that meet their research needs. In addition, corpus builders demand tools and models that will help them carry out citation functions annotation effectively. Most of previous studies annotated the citations manually in different ways, which is often time-consuming and domain dependent. To overcome these challenges, in this paper we propose new semi-automatic annotation for citation functions classification. The proposed approach builds an annotated corpus from the citation sentences. The effectiveness of the approach is compared with existing machine-learning methods. The results indicate that our approach outperforms other methods in terms of accuracy, precision and recall.