A signature approach to patent classification

Dilesha Seneviratne*, Shlomo Geva, Guido Zuccon, Gabriela Ferraro, Timothy Chappell, Magali Meireles

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    9 Citations (Scopus)

    Abstract

    We propose a document signature approach to patent classification. Automatic patent classification is a challenging task because of the fast growing number of patent applications filed every year and the complexity, size and nested hierarchical structure of patent taxonomies. In our proposal, the classification of a target patent is achieved through a k-nearest neighbour search using Hamming distance on signatures generated from patents; the classification labels of the retrieved patents are weighted and combined to produce a patent classification code for the target patent. The use of this method is motivated by the fact that intuitively document signatures are more efficient than previous approaches for this task that considered the training of classifiers on the whole vocabulary feature set. Our empirical experiments also demonstrate that the combination of document signatures and k-nearest neighbours search improves classification effectiveness, provided that enough data is used to generate signatures.

    Original languageEnglish
    Title of host publicationInformation Retrieval Technology - 11th Asia Information Retrieval Societies Conference, AIRS 2015, Proceedings
    EditorsFalk Scholer, Guido Zuccon, Shlomo Geva, Aixin Sun, Hideo Joho, Peng Zhang
    PublisherSpringer Verlag
    Pages413-419
    Number of pages7
    ISBN (Print)9783319289397
    DOIs
    Publication statusPublished - 2015
    Event11th Asia Information Retrieval Societies Conference, AIRS 2015 - Brisbane, Australia
    Duration: 2 Dec 20154 Dec 2015

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9460
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference11th Asia Information Retrieval Societies Conference, AIRS 2015
    Country/TerritoryAustralia
    CityBrisbane
    Period2/12/154/12/15

    Fingerprint

    Dive into the research topics of 'A signature approach to patent classification'. Together they form a unique fingerprint.

    Cite this