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Yayın A FST description of noun and verb morphology of Azarbaijani Turkish(Association for Computational Linguistics (ACL), 2021) Ehsani, Razieh; Özenç, Berke; Solak, Ercan; Drewes F.We give a FST description of nominal and finite verb morphology of Azarbaijani Turkish. We use a hybrid approach where nominal inflection is expressed as a slot-based paradigm and major parts of verb inflection are expressed as optional paths on the FST. We collapse adjective and noun categories in a single nominal category as they behave similarly as far as their paradigms are concerned. Thus, we defer a more precise identification of POS to further down the NLP pipeline.Yayın Automatic propbank generation for Turkish(Incoma Ltd, 2019-09) Ak, Koray; Yıldız, Olcay TanerSemantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears a semantic role. It provides an extensive dataset to enhance NLP applications such as information retrieval, machine translation, information extraction, and question answering. However, creating SRL models are difficult. Even in some languages, it is infeasible to create SRL models that have predicate-argument structure due to lack of linguistic resources. In this paper, we present our method to create an automatic Turkish PropBank by exploiting parallel data from the translated sentences of English PropBank. Experiments show that our method gives promising results. © 2019 Association for Computational Linguistics (ACL).Yayın An open, extendible, and fast Turkish morphological analyzer(Incoma Ltd, 2019-09) Yıldız, Olcay Taner; Avar, Begüm; Ercan, GökhanIn this paper, we present a two-level morphological analyzer for Turkish which 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 a comprehensive lexicon of 54,000 bare-forms including 19,000 proper nouns, our morphological analyzer is amongst 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.
