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Word Embeddings for Multi-label Document Classification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43949732" target="_blank" >RIV/49777513:23520/17:43949732 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.26615/978-954-452-049-6_057" target="_blank" >http://dx.doi.org/10.26615/978-954-452-049-6_057</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.26615/978-954-452-049-6_057" target="_blank" >10.26615/978-954-452-049-6_057</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Word Embeddings for Multi-label Document Classification

  • Original language description

    In this paper, we analyze and evaluate word embeddings for representation of longer texts in the multi-label document classification scenario. The embeddings are used in three convolutional neural network topologies. The experiments are realized on the Czech ČTK and English Reuters-21578 standard corpora. We compare the results of word2vec static and trainable embeddings with randomly initialized word vectors. We conclude that initialization does not play an important role for classification. However, learning of word vectors is crucial to obtain good results.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20204 - Robotics and automatic control

Result continuities

  • Project

    <a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

  • ISBN

    978-954-452-048-9

  • ISSN

    1313-8502

  • e-ISSN

    neuvedeno

  • Number of pages

    7

  • Pages from-to

    431-437

  • Publisher name

    INCOMA Ltd.

  • Place of publication

    Shoumen, BULGARIA

  • Event location

    Varna, Bulgaria

  • Event date

    Sep 2, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article