期刊名称：Majalah Iptek = IPTEK : The Journal for Technology and Science
印刷版ISSN：0853-4098
电子版ISSN：2088-2033
出版年度：2019
卷号：30
期号：2
页码：49-51
DOI：10.12962/j20882033.v30i2.5008
出版社：IPTEK
摘要：Scientific articles cited by other researchers have an impact on increasing author credibility. However, the citation
process may be misused to unnaturally raise a bibliometric indicator value such as researcher’s h-index. Researchers may
overly cites their own works, referred as self-citation, even though the topic of the references are not related to the current
article. Further misconduct is excessive citations on the works of peoples related to the researcher which can be coercive or
not, referred as conflict of interest (CoI). The proposed method uses a deep learning approach, Siamese Long Short- Term
Memory (LSTM), to recognize subject similarities between a scientific article and its references. Standard text similarity
fails to do so because contextual relatedness of sentences in the articles need some learning process. Siamese-LSTM learns
contextual relatedness of sentences in the article using two identical LSTM. Steps of the proposed method are (i) wordembedding
to get weight values of terms but still considers their semantic relations, (ii) k-means clustering to generate
training data for reducing time complexity in Siamese-LSTM learning of scientific articles, (iii) learns Siamese-LSTM
weight from training data to identify contextual relatedness of sentences, (iv) calculate similarity of a scientific article with
its references based on Siamese-LSTM. The empirical experiments are used to analyze similarity values and the possibility
for conflict of interest in an article.
关键词：Citation;Conflict of Interest;Scientific Text;Deep Learning;Similarity;Text Processing
