Dating texts by multi-class classification with sliding time intervals

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    We propose a practical method to date texts by classification with sliding time intervals (STI). This further explores the advantage of multi-class text classification, while drawing upon temporal characteristics in the training corpus. Extensive experiments were made on English and medieval Irish texts. Results showed that our STI dating method significantly outperformed classifiers with fixed time intervals (FTI). The Naïve Bayes Multinomial (NBM) with STI achieved the state-of-the-art dating precision on DTE Subtask 2 though only involving features of n-gram characters and words. Experiments on dating long documents and further analysis also indicated some promising points for further text dating research and other humanities fields.
    Original languageEnglish
    Pages1
    Number of pages6
    DOIs
    Publication statusPublished - 27 Feb 2018
    EventInternational Congress on Image and Signal Processing, BioMedical Engineering and Informatics - Shanghai, China
    Duration: 14 Oct 201716 Oct 2017

    Conference

    ConferenceInternational Congress on Image and Signal Processing, BioMedical Engineering and Informatics
    Abbreviated titleCISP-BMEI 2017
    CountryChina
    CityShanghai
    Period14/10/201716/10/2017

      Research areas

    • Bayes methods, Naïve Bayes Multinomial, sliding time intervals, medieval Irish, text dating, machine learning, annals

    ID: 148902284