All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Word classes in computational linguistics and artificial intelligence

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F24%3A73628826" target="_blank" >RIV/61989592:15210/24:73628826 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1093/oxfordhb/9780198852889.013.41" target="_blank" >http://dx.doi.org/10.1093/oxfordhb/9780198852889.013.41</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1093/oxfordhb/9780198852889.013.41" target="_blank" >10.1093/oxfordhb/9780198852889.013.41</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Word classes in computational linguistics and artificial intelligence

  • Original language description

    Word classes, or parts of speech, are groupings of words that share common characteristics, both syntactic and semantic. In the fields of Natural Language Processing and Artificial Intelligence, word classes have been seen as the first level of annotation for text tokens within a text processing pipeline: (POS) tagging. These POS tags were originally conceived as the first step for syntactic processing, but are often used to prepare a stream of unstructured text in for the purpose of natural language understanding. The level of understanding achieved by such a system can be as deep as achieving a semantically motivated representation of meaning or as superficial as recognising the usage of certain types of words that are important for the task at hand. The importance of word classes in these fields is that they allow a level of abstraction that can alleviate data sparsity and are generally quite accurate, especially when considering left and right contexts and other contextual information. Further, they can help in suggesting the function of a token within a sentence, as well as suggesting its semantic type (such as event, attribute or referent). Scientists and practitioners in NLP and AI are consumers of word classes. However, the field of Computational Linguistics has slightly different goals that overlap more with the interest of linguists. Computational linguists ask questions that a linguist might about word classes but the answers to these questions are arrived at either by methods employed in Grammar Engineering or big data methods employed in Natural Language Processing.

  • Czech name

  • Czech description

Classification

  • Type

    C - Chapter in a specialist book

  • CEP classification

  • OECD FORD branch

    60203 - Linguistics

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2024

  • Confidentiality

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

Data specific for result type

  • Book/collection name

    The Oxford Handbook of Word Classes

  • ISBN

    978-0-19-885288-9

  • Number of pages of the result

    17

  • Pages from-to

    930-946

  • Number of pages of the book

    1136

  • Publisher name

    Oxford University Press

  • Place of publication

    Oxford

  • UT code for WoS chapter