Towards computation of novel ideas from corpora of scientific text

Liu, Haixia; Goulding, James; Brailsford, Tim

Abstract

© Springer International Publishing Switzerland 2015. In this work we present a method for the computation of novel ‘ideas’ from corpora of scientific text. The system functions by first detecting concept noun-phrases within the titles and abstracts of publications using Part-Of-Speech tagging, before classifying these into sets of problem and solution phrases via a target-word matching approach. By defining an idea as a co-occurring pair, Known-idea triples can be constructed through the additional assignment of a relevance value (computed via either phrase co-occurrence or an ‘idea frequency-inverse document frequency’ score). The resulting triples are then fed into a collaborative filtering algorithm, where problem-phrases are considered as users and solution-phrases as the items to be recommended. The final output is a ranked list of novel idea candidates, which hold potential for researchers to integrate into their hypothesis generation processes. This approach is evaluated using a subset of publications from the journal Science, with precision, recall and F-Measure results for a variety of model parametrizations indicating that the system is capable of generating useful novel ideas in an automated fashion.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
• Acceptance Date: Jan 1, 2015
• Publication Date: Jan 1, 2015
• Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Print ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 9285
• Pages: 541-556
• Series Title: Lecture Notes in Computer Science
• Book Title: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science
• DOI: https://doi.org/10.1007/978-3-319-23525-7_33
• Keywords: idea mining, text mining, natural language processing, recommender systems, collaborative filtering
• Public URL: https://uwe-repository.worktribe.com/output/842965