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Annif Analyzer Shootout: Comparing text lemmatization methods for automated subject indexing

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AJXM7A3CF" target="_blank" >RIV/00216208:11320/22:JXM7A3CF - isvavai.cz</a>

  • Result on the web

    <a href="https://journal.code4lib.org/articles/16719" target="_blank" >https://journal.code4lib.org/articles/16719</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Annif Analyzer Shootout: Comparing text lemmatization methods for automated subject indexing

  • Original language description

    Automated text classification is an important function for many AI systems relevant to libraries, including automated subject indexing and classification. When implemented using the traditional natural language processing (NLP) paradigm, one key part of the process is the normalization of words using stemming or lemmatization, which reduces the amount of linguistic variation and often improves the quality of classification. In this paper, we compare the output of seven different text lemmatization algorithms as well as two baseline methods. We measure how the choice of method affects the quality of text classification using example corpora in three languages. The experiments have been performed using the open source Annif toolkit for automated subject indexing and classification, but should generalize also to other NLP toolkits and similar text classification tasks. The results show that lemmatization methods in most cases outperform baseline methods in text classification particularly for Finnish and Swedish text, but not English, where baseline methods are most effective. The differences between lemmatization methods are quite small. The systematic comparison will help optimize text classification pipelines and inform the further development of the Annif toolkit to incorporate a wider choice of normalization methods.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    The Code4Lib Journal

  • ISSN

    1940-5758

  • e-ISSN

    2076-3425

  • Volume of the periodical

  • Issue of the periodical within the volume

    54

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    8

  • Pages from-to

    1-8

  • UT code for WoS article

    000856268200001

  • EID of the result in the Scopus database

    2-s2.0-85138622509