Semantic Analysis for Automated Evaluation of the Potential Impact of
Research Articles
- URL: http://arxiv.org/abs/2104.12869v1
- Date: Mon, 26 Apr 2021 20:37:13 GMT
- Title: Semantic Analysis for Automated Evaluation of the Potential Impact of
Research Articles
- Authors: Neslihan Suzen, Alexander Gorban, Jeremy Levesley and Evgeny Mirkes
- Abstract summary: This paper presents a novel method for vector representation of text meaning based on information theory.
We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus.
We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
- Score: 62.997667081978825
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Can the analysis of the semantics of words used in the text of a scientific
paper predict its future impact measured by citations? This study details
examples of automated text classification that achieved 80% success rate in
distinguishing between highly-cited and little-cited articles. Automated
intelligent systems allow the identification of promising works that could
become influential in the scientific community.
The problems of quantifying the meaning of texts and representation of human
language have been clear since the inception of Natural Language Processing.
This paper presents a novel method for vector representation of text meaning
based on information theory and show how this informational semantics is used
for text classification on the basis of the Leicester Scientific Corpus.
We describe the experimental framework used to evaluate the impact of
scientific articles through their informational semantics. Our interest is in
citation classification to discover how important semantics of texts are in
predicting the citation count. We propose the semantics of texts as an
important factor for citation prediction.
For each article, our system extracts the abstract of paper, represents the
words of the abstract as vectors in Meaning Space, automatically analyses the
distribution of scientific categories (Web of Science categories) within the
text of abstract, and then classifies papers according to citation counts
(highly-cited, little-cited).
We show that an informational approach to representing the meaning of a text
has offered a way to effectively predict the scientific impact of research
papers.
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