Partridge: An effective system for the automatic classification of the types of academic papers

Authors Organisations
Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXX
Subtitle of host publicationIncorporating Applications and Innovations in Intelligent Systems XXI Proceedings of AI-2013, The Thirty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
EditorsMax Bramer, Miltos Petridis
PublisherSpringer Nature
ISBN (Electronic)978-3-319-02621-3
ISBN (Print)978-3-319-02620-6
StatePublished - Dec 2013
EventAI-2013: The Thirty-third SGAI International Conference - Cambridge, United Kingdom of Great Britain and Northern Ireland
Duration: 10 Dec 201312 Dec 2013


ConferenceAI-2013: The Thirty-third SGAI International Conference
CountryUnited Kingdom of Great Britain and Northern Ireland
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Partridge is a system that enables intelligent search for academic papers by allowing users to query terms within sentences designating a particular core scientific concept (e.g. Hypothesis, Result, etc). The system also automatically classifies papers according to article types (e.g. Review, Case Study). Here, we focus on the latter aspect of the system. For each paper, Partridge automatically extracts the full paper content from PDF files, converts it to XML, determines sentence boundaries, automatically labels the sentences with core scientific concepts, and then uses a random forest model to classify the paper type. We show that the type of a paper can be reliably predicted by a model which analyses the distribution of core scientific concepts within the sentences of the paper. We discuss the appropriateness of many of the existing paper types used by major journals, and their corresponding distributions. Partridge is online and available for use, includes a browser-friendly bookmarklet for new paper submission, and demonstrates a range of possibilities for more intelligent search in the scientific literature. The Partridge instance and further information about the project can be found at