Refining Kea++ automatic keyphrase assignment

• Published in: Journal of information science Vol. 40; no. 4; pp. 446 - 459
• Main Authors: Irfan, Rabia, Khan, Sharifullah, Qamar, Ali Mustafa, Bloodsworth, Peter Charles
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.08.2014; Sage Publications; Bowker-Saur Ltd
• Subjects: Artificial intelligence; Automation; Classification; Effectiveness studies; Exact sciences and technology; General aspects; Information and communication sciences; Information science. Documentation; Methodology; Programming languages; Sciences and techniques of general use; Tasks; Taxonomy; Texts; Vocabularies & taxonomies; subject classification; keyphrase extraction; keyphrase indexing; Keyphrase assignment; taxonomy; Assignment; Classification; Extraction; Topic; Indexing; Automatic processing
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551514529054

Keyphrases facilitate finding the right information in digital sources. Keyphrase assignment is the alignment of documents or text with keyphrases of any standard taxonomy/classification system. Kea++ is an automatic keyphrase assignment tool using a machine learning-based technique. However, it does not effectively exploit the hierarchical relations that exist in its input taxonomy and returns noise in its results. The refinement methodology was designed as a top layer of Kea++ in order to fine tune its results. It was an initial step and focused on a single Computing domain. It was neither validated on multiple domains nor evaluated to determine whether the improvement in the results is significant or not. The aim of this task was to solidify the refinement methodology. The main contributions of this work are (a) to extend the methodology for multiple domains and (b) to statistically verify that the improvement in the Kea++ results is significant.