Abstract
We describe a novel search engine for scientific literature. The system allows for sentence-level search starting from portable document format (PDF) files, and integrates text and image search, thus, for example, facilitating the retrieval of information present in tables and figures using both image and caption content. In addition, the system allows the user to generate in an intuitive manner complex queries for search terms that are related through particular grammatical (and thus implicitly semantic) relations. Grid processing techniques are used to parallelise the analysis of large numbers of scientific papers. We are currently conducting user evaluations, but here we report some preliminary evaluation and comparison with Google Scholar, demonstrating the potential utility of the novel features. Finally, we discuss future work and the potential and complementarity of the system for patent search.
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Acknowledgements
This work was supported in part by a BBSRC e-Science programme grant to the University of Cambridge (FlySlip), and a STFC miniPIPSS grant to the University of Cambridge and iLexIR Ltd (Scalable and Robust Grid-based Text Mining of Scientific Papers). This chapter is an extended version of one which appeared in the proceedings of the annual North American Association for Computational Linguistics conference proceedings, demonstration session, in June 2010.
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Briscoe, T. et al. (2011). Intelligent Information Access from Scientific Papers. In: Lupu, M., Mayer, K., Tait, J., Trippe, A. (eds) Current Challenges in Patent Information Retrieval. The Information Retrieval Series, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19231-9_16
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DOI: https://doi.org/10.1007/978-3-642-19231-9_16
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