Gökhan Ercan


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Morpholex Turkish: A Morphological Lexicon for Turkish
Bilge Arican | Aslı Kuzgun | Büşra Marşan | Deniz Baran Aslan | Ezgi Saniyar | Neslihan Cesur | Neslihan Kara | Oguzhan Kuyrukcu | Merve Ozcelik | Arife Betul Yenice | Merve Dogan | Ceren Oksal | Gökhan Ercan | Olcay Taner Yıldız
Proceedings of Globalex Workshop on Linked Lexicography within the 13th Language Resources and Evaluation Conference

MorphoLex is a study in which root, prefix and suffixes of words are analyzed. With MorphoLex, many words can be analyzed according to certain rules and a useful database can be created. Due to the fact that Turkish is an agglutinative language and the richness of its language structure, it offers different analyzes and results from previous studies in MorphoLex. In this study, we revealed the process of creating a database with 48,472 words and the results of the differences in language structure.


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An Open, Extendible, and Fast Turkish Morphological Analyzer
Olcay Taner Yıldız | Begüm Avar | Gökhan Ercan
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

In this paper, we present a two-level morphological analyzer for Turkish. The morphological analyzer consists of five main components: finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache. We use Java language to implement finite state machine logic and rule engine, Xml language to describe the finite state transducer rules of the Turkish language, which makes the morphological analyzer both easily extendible and easily applicable to other languages. Empowered with the comprehensiveness of a lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer presents one of the most reliable analyzers produced so far. The analyzer is compared with Turkish morphological analyzers in the literature. By using LRU cache and a trie data structure, the system can analyze 100,000 words per second, which enables users to analyze huge corpora in a few hours.


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AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness
Gökhan Ercan | Olcay Taner Yıldız
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.