---
res:
  bibo_abstract:
  - "Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a
    key requirement for both linguistic research and subsequent automated natural
    language processing (NLP) tasks. This problem is commonly tackled using machine
    learning methods, i.e., by training a POS tagger on a sufficiently large corpus
    of labeled data. \r\nWhile the problem of POS tagging can essentially be considered
    as solved for modern languages, historical corpora turn out to be much more difficult,
    especially due to the lack of native speakers and sparsity of training data. Moreover,
    most texts have no sentences as we know them today, nor a common orthography.\r\nThese
    irregularities render the task of automated POS tagging more difficult and error-prone.
    Under these circumstances, instead  of forcing the POS tagger to predict and commit
    to a single tag, it should be enabled to express its uncertainty. In this paper,
    we consider POS tagging within the framework of set-valued prediction, which allows
    the POS tagger to express its uncertainty via predicting a set of candidate POS
    tags instead of guessing a single one. The goal is to guarantee a high confidence
    that the correct POS tag is included while keeping the number of candidates small.\r\nIn
    our experimental study, we find that extending state-of-the-art POS taggers to
    set-valued prediction yields more precise and robust taggings, especially for
    unknown words, i.e., words not occurring in the training data.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Stefan Helmut
      foaf_name: Heid, Stefan Helmut
      foaf_surname: Heid
      foaf_workInfoHomepage: http://www.librecat.org/personId=39640
    orcid: 0000-0002-9461-7372
  - foaf_Person:
      foaf_givenName: Marcel Dominik
      foaf_name: Wever, Marcel Dominik
      foaf_surname: Wever
      foaf_workInfoHomepage: http://www.librecat.org/personId=33176
    orcid: ' https://orcid.org/0000-0001-9782-6818'
  - foaf_Person:
      foaf_givenName: Eyke
      foaf_name: Hüllermeier, Eyke
      foaf_surname: Hüllermeier
      foaf_workInfoHomepage: http://www.librecat.org/personId=48129
  dct_date: 2020^xs_gYear
  dct_language: eng
  dct_publisher: episciences@
  dct_title: Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued
    Prediction@
...