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Extracting semantic information from patent claims using phrasal structure annotations

    Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

    Abstract

    The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity of a patent application. Patent information retrieval research is thus growing quickly to support document analysis across multiple domains and information systems. One of the big challenges in patent analysis is the identification of the elements of innovation (concepts, processes, materials) and the relations between them, in the patent text. This paper presents a method for extracting semantic information from patent claims by using semantic annotations on phrasal structures, abstracting domain ontology information and outputting ontology-friendly structures to achieve generalization. An extraction system built upon the method is briefly evaluated on a document sample from INPI, the Brazilian patent office, a challenging information source.

    Original languageEnglish
    Title of host publicationProceedings - 2014 Brazilian Conference on Intelligent Systems, BRACIS 2014
    Place of PublicationDanvers, MA
    PublisherIEEE
    Pages31-36
    Number of pages6
    ISBN (Electronic)9781479956180
    DOIs
    Publication statusPublished - 15 Dec 2014
    Event3rd Brazilian Conference on Intelligent Systems, BRACIS 2014 - Sao Carlos, Sao Paulo, Brazil
    Duration: 19 Oct 2014 → 23 Oct 2014

    Conference

    Conference3rd Brazilian Conference on Intelligent Systems, BRACIS 2014
    Country/TerritoryBrazil
    CitySao Carlos, Sao Paulo
    Period19/10/14 → 23/10/14

    Keywords

    • Machine Learning
    • Natural Language Processing
    • Patent Information Extraction
    • Semantic Relation Extraction
    • Semantic Segmentation

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