Researcher:
Cirik, Volkan

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Master Student

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Volkan

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Cirik

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Cirik, Volkan

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20132

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Researcher Of Publications: search.filters.isAuthorOfPublication.3293f6da-76a1-4090-823c-3b19f6cc7fd4

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    The AI-KU system at the SPMRL 2013 shared task: unsupervised features for dependency parsing
    (Association for Computational Linguistics (ACL), 2013) N/A; N/A; Cirik, Volkan; Şensoy, Hüsnü; Master Student; PhD Student; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; N/A; N/A
    We propose the use of the word categories and embeddings induced from raw text as auxiliary features in dependency parsing. To induce word features, we make use of contextual, morphologic and orthographic properties of the words. To exploit the contextual information, we make use of substitute words, the most likely substitutes for target words, generated by using a statistical language model. We generate morphologic and orthographic properties of word types in an unsupervised manner. We use a co-occurrence model with these properties to embed words onto a 25-dimensional unit sphere. The AI-KU system shows improvements for some of the languages it is trained on for the first Shared Task of Statistical Parsing of Morphologically Rich Languages.
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    AI-KU: using substitute vectors and co-occurrence modeling for word sense induction and disambiguation
    (Association for Computational Linguistics (ACL), 2013) N/A; N/A; N/A; Department of Computer Engineering; Başkaya, Osman; Cirik, Volkan; Yüret, Deniz; Master Student; Master Student; Master Student; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 179996
    Word sense induction aims to discover different senses of a word from a corpus by using unsupervised learning approaches. Once a sense inventory is obtained for an ambiguous word, word sense discrimination approaches choose the best-fitting single sense for a given context from the induced sense inventory. However, there may not be a clear distinction between one sense and another, although for a context, more than one induced sense can be suitable. Graded word sense method allows for labeling a word in more than one sense. In contrast to the most common approach which is to apply clustering or graph partitioning on a representation of first or second order co-occurrences of a word, we propose a system that creates a substitute vector for each target word from the most likely substitutes suggested by a statistical language model. Word samples are then taken according to probabilities of these substitutes and the results of the co-occurrence model are clustered. This approach outperforms the other systems on graded word sense induction task in SemEval-2013.