Author:
Mostafa Alli
Affiliation:
Tsinghua University, China
Keyword(s):
Paper Prediction, Common Coauthors, Textual Reference, Visual Feature, Table, Figure, Boolean Function, Sensitivity, Scientific Stop Word.
Abstract:
In this paper we introduce a mechanism to find similar papers of an author, based on the author’s previous
publications. In other words, since the author(s) of a paper are more likely to publish similar work(s) to their
paper, we use this intuition to seek related papers based on the visual similarity of those papers. The visuality
here is the figures and or tables that are commonly used by authors to describe their method structure and/or
the result of their experiments. Since similar works of authors are focused on solving similar problems as well
as developing and improving similar techniques, we noticed that comparing these visual features among their
publications would help to spot most similar papers of those authors. We call our method, Similarity of Textual
References to Visual Features which means, we compare parts of content of any two arbitrary papers that have
references to any figures and/or tables. In our experiment we show that how we can use this similarity together
w
ith other factors of a paper to form a Boolean function which helps to build an indexation for papers based
on the number of their authors. In this way, we omit time consuming process of papers’ content determined
analysis, such as, textual content analysis, building coauthor network, citation network etc. In addition, our
Boolean function has the ability of adjusting level of Sensitivity. If we want to achieve higher accuracy of
similar papers, the Boolean function needs to be enabled for more
conditions.
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