List of results published directly linked with the projects co-funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Program (MDM-2015-0502).

List of publications acknowledging the funding in Scopus.

The record for each publication will include access to postprints (following the Open Access policy of the program), as well as datasets and software used. Ongoing work with UPF Library and Informatics will improve the interface and automation of the retrieval of this information soon.

The MdM Strategic Research Program has its own community in Zenodo for material available in this repository   as well as at the UPF e-repository   

 

 

Back Dr. Inventor Text Mining Framework

Dr. Inventor Text Mining Framework is a Java library that integrates several Document Engeneering and Natural Language Processing tools customized to enable and ease the analysis of the textual contents of scientific publications.


Dr. Inventor Text Mining Framework is a standalone Java library that enable users to process the contents of papers both in PDF and JATS XML format. Once imported a paper from a local file or a remote URL, the Framework automatically extracts and characterizes several aspects including:

  • Structural elements: title, abstract, hierarchy of sections, sentences inside each section, bibliographic entries
  • Bibliographic entries are parsed and enriched by accessing external web services (Bibsonomy, CrossRef, FreeCite, Google Scholar)
  • Inline citations are spotted and linked to the respective bibliographic entry
  • The dependency tree is built from each sentence by considering inline citations
  • The discoursive category of each sentence is identified among: Background, Challenge, Approach, Outcome and Future Work
  • BabelNet synsets are spotted inside the contents of each sentence thanks to Babelfy
  • Subject-Verb-Object graphs are build to represent the contents of paper excerpts (the connectedness of these graphs is enhanced thanks to coreference resolution)
  • Relevant sentences are selected with respect to several criteria to build extractive summaries of a paper
  • etc.

Ronzano, F., & Saggion, H.: Dr. Inventor Framework: Extracting Structured Information from Scientific  Publications. Discovery Science (pp. 209-220). Springer International Publishing. (2015)

Link: http://backingdata.org/dri/library/

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