TITLE:
Low-Power Themes Classifier (LPTC): A Human-Expert-Based Approach for Classification of Scientific Papers/Theses with Low-Power Theme
AUTHORS:
Mohsen Abasi, Mohammad Bagher Ghaznavi-Ghoushchi
KEYWORDS:
Natural Language Processing (NLP); Semantic Web; Term Extraction; Text Categorization; Resource Description Framework (RDF); Low-Power Theme
JOURNAL NAME:
Intelligent Information Management,
Vol.4 No.6,
November
30,
2012
ABSTRACT: Document classification is widely applied in many scientific areas and academic environments, using NLP techniques and term extraction algorithms like CValue, TfIdf, TermEx, GlossEx, Weirdness and the others like. Nevertheless, they mainly have weaknesses in extracting most important terms when input text has not been rectified grammatically, or even has non-alphabetic methodical and math or chemical notations, and cross-domain inference of terms and phrases. In this paper, we propose a novel Text-Categorization and Term-Extraction method based on human-expert choice of classified categories. Papers are the training phase substances of the proposed algorithm. They have been already labeled with some scientific pre-defined field specific categories, by a human expert, especially one with high experiences and researches and surveys in the field. Our approach thereafter extracts (concept) terms of the labeled papers of each category and assigns all to the category. Categorization of test papers is then applied based on their extracted terms and further comparing with each category’s terms. Besides, our approach will produce semantic enabled outputs that are useful for many goals such as knowledge bases and data sets complement of the Linked Data cloud and for semantic querying of them by some languages such as SparQL. Besides, further finding classified papers’ gained topic or class will be easy by using URIs contained in the ontological outputs. The experimental results, comparing LPTC with five well-known term extraction algorithms by measuring precision and recall, show that categorization effectiveness can be achieved using our approach. In other words, the method LPTC is significantly superior to CValue, TfIdf, TermEx, GlossEx and Weirdness in the target study. As well, we conclude that higher number of papers for training, even higher precision we have.