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Dr. Öğr. Üyesi

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Now showing 1 - 3 of 3
  • PublicationOpen Access
    Diffused label popagation based transductive classification algorithm for Wwrd sense disambiguation
    (2019-07) Kocaman, G.; Gerek, A.; Altınel, B.; Ganiz, M.C.; ŞİPAL, BİLGE
    A major natural language processing problem, word sense disambiguation is the task of identifying the correct sense of a polysemous word based on its context. In terms of machine learning, this can be considered as a supervised classification problem. A better alternative can be the use of semi-supervised classifiers since labeled data is usually scarce yet we can access large quantities of unlabeled textual data. We propose an improvement to Label Propagation which is a well-known transductive classification algorithm for word sense disambiguation. Our approach make use of a semantic diffusion kernel. We name this new algorithm as diffused label propagation algorithm (DILP). We evaluate our proposed algorithm with experiments utilizing various sizes of training sets of disambiguated corpora. With these experiments we try to answer the following questions: 1. Does our algorithm with semantic kernel formulation yield higher classification performance than the popular kernels? 2. Under which conditions does a kernel design perform better than others? 3. What kind of regularization methods result with better performance? Our experiments demonstrate that our approach can outperform baseline in terms of accuracy in several conditions.
  • Publication
    Word sense disambiguation using semantic kernels with class-based term values
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2019) Altınel, Berna; Ganil, Murat Can; Erkaya, Erencan; Yücedağ, Onur Can; Doğan, Muhammed Ali; ŞİPAL, BİLGE
    In this study, we propose several semantic kernels for word sense disambiguation (WSD). Our approaches adapt the intuition that class-based term values help in resolving ambiguity of polysemous words in WSD. We evaluate our proposed approaches with experiments, utilizing various sizes of training sets of disambiguated corpora (SensEval(1)). With these experiments we try to answer the following questions: 1.) Do our semantic kernel formulations yield higher classification performance than traditional linear kernel?, 2.) Under which conditions a kernel design performs better than others?, 3.) Does the addition of class labels into standard term-document matrix improve the classification accuracy?, 4.) Is their combination superior to either type?, 5.) Is ensemble of these kernels perform better than the baseline?, 6.) What is the effect of training set size? Our experiments demonstrate that our kernel-based WSD algorithms can outperform baseline in terms of F-score.
  • Publication
    On the regularity of the monomial point of a border basis scheme
    (2020) Kreuzer, M.; Long, L.N.; ŞİPAL, BİLGE
    The border basis scheme BO parametrizes all 0-dimensional ideals in K[x1, … , xn] , where K is an arbitrary field, which have a border basis with respect to a given order ideal of terms O. Its vanishing ideal I(BO) is generated by quadratic equations which are easy to describe, and it contains a unique monomial point, namely the point corresponding to the monomial ideal generated by the terms outside O. Based on a detailed study of the generators of I(BO) , we describe a K-basis of the cotangent space m/ m2, where m is the maximal ideal of the monomial point. Moreover, we provide an efficient algorithm to compute such a basis and use it to characterize the regularity of the monomial point. © 2020, The Managing Editors.