Abstract
Biomedical literature is composed of an ever increasing number of publications in natural language. Patents are a relevant fraction of those, being important sources of information due to all the curated data from the granting process. However, their unstructured data turns the search of information a challenging task. To surpass that, Biomedical text mining (BioTM) creates methodologies to search and structure that data. Several BioTM techniques can be applied to patents. From those, Information Retrieval is the process where relevant data is obtained from collections of documents. In this work, a patent pipeline was developed and integrated into @Note2, an open-source computational framework for BioTM. This integration allows to run further BioTM tools over the patent documents, including Information Extraction processes as Named Entity Recognition or Relation Extraction.
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