Towards computation of novel ideas from corpora of scientific text

Liu, Haixia and Goulding, James and Brailsford, Tim (2015) Towards computation of novel ideas from corpora of scientific text. In: Machine Learning and Knowledge Discovery in Databases. Springer Verlag, Cham, Switzerland, pp. 541-556.

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Abstract

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 <problem,solution> 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.

Item Type: Book Section
Additional Information: Liu H., Goulding J., Brailsford T. (2015) Towards Computation of Novel Ideas from Corpora of Scientific Text. In: Appice A., Rodrigues P., Santos Costa V., Gama J., Jorge A., Soares C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science, vol 9285. The final authenticated version is available online at https://doi.org/10.1007/978-3-319-23525-7_33.
Keywords: Idea mining, Text mining, Natural language processing, Recommender systems, Collaborative filtering
Schools/Departments: University of Nottingham, UK > Faculty of Social Sciences > Nottingham University Business School
University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-319-23525-7_33
Depositing User: Goulding, James
Date Deposited: 11 Dec 2018 13:42
Last Modified: 11 Jan 2019 17:05
URI: http://eprints.nottingham.ac.uk/id/eprint/55719

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