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Coupled-Tensor Generated Word Embeddings and Their Composition

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00367813" target="_blank" >RIV/68407700:21230/23:00367813 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-031-37717-4_49" target="_blank" >https://doi.org/10.1007/978-3-031-37717-4_49</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-37717-4_49" target="_blank" >10.1007/978-3-031-37717-4_49</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Coupled-Tensor Generated Word Embeddings and Their Composition

  • Original language description

    Contemporary methods of computing vector-space embeddings of words are able to accurately capture both their semantic and syntactic properties. Methods for computing n-gram embeddings do, however, come with downsides. They either require high resources during training or estimation, or come with other disadvantages, such as loss of information about individual positions of words in phrases. We propose two novel approaches to training word vectors enabling a composition of word embeddings into n-gram embeddings. Both methods are based on coupled CP decomposition of tensors that are generated by a sequence of time-shifted word embeddings. We compare our methods with SGNS and show that they provide superior performance on word-analogy tasks.

  • 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

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Lecture Notes in Networks and Systems

  • ISBN

    978-3-031-37716-7

  • ISSN

    2367-3370

  • e-ISSN

  • Number of pages

    15

  • Pages from-to

    753-767

  • Publisher name

    Springer International Publishing AG

  • Place of publication

    Cham

  • Event location

    Londýn

  • Event date

    Jun 22, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article