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Yayın MorAz: An open-source morphological analyzer for Azerbaijani Turkish(Association for Computational Linguistics (ACL), 2018) Özenç, Berke; Ehsani, Razieh; Solak, ErcanMorAz is an open-source morphological analyzer for Azerbaijani Turkish. The analyzer is available through both as a website for interactive exploration and as a RESTful web service for integration into a natural language processing pipeline. MorAz implements the morphology of Azerbaijani Turkish following a two-level approach using Helsinki finite-state transducer and wraps the analyzer with python scripts in a Django instance.Yayın Constructing a Turkish constituency parse treeBank(Springer Verlag, 2016) Yıldız, Olcay Taner; Solak, Ercan; Çandır, Şemsinur; Ehsani, Razieh; Görgün, OnurIn this paper, we describe our initial efforts for creating a Turkish constituency parse treebank by utilizing the English Penn Treebank. We employ a semiautomated approach for annotation. In our previouswork [18], the English parse trees were manually translated to Turkish. In this paper, the words are semi-automatically annotated morphologically. As a second step, a rule-based approach is used for refining the parse trees based on the morphological analyses of the words. We generated Turkish phrase structure trees for 5143 sentences from Penn Treebank that contain fewer than 15 tokens. The annotated corpus can be used in statistical natural language processing studies for developing tools such as constituency parsers and statistical machine translation systems for Turkish.Yayın Chunking in Turkish with conditional random fields(Springer-Verlag, 2015-04-14) Yıldız, Olcay Taner; Solak, Ercan; Ehsani, Razieh; Görgün, OnurIn this paper, we report our work on chunking in Turkish. We used the data that we generated by manually translating a subset of the Penn Treebank. We exploited the already available tags in the trees to automatically identify and label chunks in their Turkish translations. We used conditional random fields (CRF) to train a model over the annotated data. We report our results on different levels of chunk resolution.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 Constructing a Turkish-English parallel treebank(Association for Computational Linguistics (ACL), 2014) Yıldız, Olcay Taner; Solak, Ercan; Görgün, Onur; Ehsani, RaziehIn this paper, we report our preliminary efforts in building an English-Turkish parallel treebank corpus for statistical machine translation. In the corpus, we manually generated parallel trees for about 5,000 sentences from Penn Treebank. English sentences in our set have a maximum of 15 tokens, including punctuation. We constrained the translated trees to the reordering of the children and the replacement of the leaf nodes with appropriate glosses. We also report the tools that we built and used in our tree translation task.
