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Examining Structure of Word Embeddings with PCA

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405584" target="_blank" >RIV/00216208:11320/19:10405584 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-030-27947-9_18" target="_blank" >http://dx.doi.org/10.1007/978-3-030-27947-9_18</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-27947-9_18" target="_blank" >10.1007/978-3-030-27947-9_18</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Examining Structure of Word Embeddings with PCA

  • Original language description

    In this paper we compare structure of Czech word embeddings for English-Czech neural machine translation (NMT), word2vec and sentiment analysis. We show that although it is possible to successfully predict part of speech (POS) tags from word embeddings of word2vec and various translation models, not all of the embedding spaces show the same structure. The information about POS is present in word2vec embeddings, but the high degree of organization by POS in the NMT decoder suggests that this information is more important for machine translation and therefore the NMT model represents it in more direct way. Our method is based on correlation of principal component analysis (PCA) dimensions with categorical linguistic data. We also show that further examining histograms of classes along the principal component is important to understand the structure of representation of information in embeddings.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • 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

    <a href="/en/project/GA18-02196S" target="_blank" >GA18-02196S: Linguistic Structure Representation in Neural Networks</a><br>

  • Continuities

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

Others

  • Publication year

    2019

  • 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 22nd International Conference on Text, Speech and Dialogue - TSD 2019

  • ISBN

    978-3-030-27946-2

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    211-223

  • Publisher name

    Springer International Publishing

  • Place of publication

    Cham

  • Event location

    Ljubljana, Slovenia

  • Event date

    Sep 11, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article