Filtreler

2012 - 20203
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Yazar: Ak, Koray × Konu: Turkishs ×

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Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Yayın
    TRopBank: Turkish PropBank V2.0
    (European Language Resources Association (ELRA), 2020-05-16) Kara, Neslihan; Aslan, Deniz Baran; Marşan, Büşra; Bakay, Özge; Ak, Koray; Yıldız, Olcay Taner
    In this paper, we present and explain TRopBank “Turkish PropBank v2.0”. PropBank is a hand-annotated corpus of propositions which is used to obtain the predicate-argument information of a language. Predicate-argument information of a language can help understand semantic roles of arguments. “Turkish PropBank v2.0”, unlike PropBank v1.0, has a much more extensive list of Turkish verbs, with 17.673 verbs in total.
  • Küçük Resim
    Yayın
    Construction of a Turkish proposition bank
    (Tubitak Scientific & Technical Research Council Turkey, 2018) Ak, Koray; Toprak, Cansu; Esgel, Volkan; Yıldız, Olcay Taner
    This paper describes our approach to developing the Turkish PropBank by adopting the semantic role-labeling guidelines of the original PropBank and using the translation of the English Penn-TreeBank as a resource. We discuss the semantic annotation process of the PropBank and language-specific cases for Turkish, the tools we have developed for annotation, and quality control for multiuser annotation. In the current phase of the project, more than 9500 sentences are semantically analyzed and predicate-argument information is extracted for 1330 verbs and 1914 verb senses. Our plan is to annotate 17,000 sentences by the end of 2017.
  • Küçük Resim
    Yayın
    Unsupervised morphological analysis using tries
    (Springer London, 2012) Ak, Koray; Yıldız, Olcay Taner
    This article presents an unsupervised morphological analysis algorithm to segment words into roots and affixes. The algorithm relies on word occurrences in a given dataset. Target languages are English, Finnish, and Turkish, but the algorithm can be used to segment any word from any language given the wordlists acquired from a corpus consisting of words and word occurrences. In each iteration, the algorithm divides words with respect to occurrences and constructs a new trie for the remaining affixes. Preliminary experimental results on three languages show that our novel algorithm performs better than most of the previous algorithms.