Overcoming the challenges in morphological annotation of Turkish in universal dependencies framework

no code implementations • EMNLP (LAW, DMR) 2021 • Talha Bedir, Karahan Şahin, Onur Gungor, Suzan Uskudarli, Arzucan Özgür, Tunga Güngör, Balkiz Ozturk Basaran

This paper presents these issues and our proposals to more accurately represent morphosyntactic information for Turkish while adhering to guidelines of UD.

Detecting Clitics Related Orthographic Errors in Turkish

no code implementations • RANLP 2019 • Ugurcan Arikan, Onur Gungor, Suzan Uskudarli

The model achieved an F1 score of 86. 67{\%} on a synthetically constructed dataset.

Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags

1 code implementation • COLING 2018 • Onur G{\"u}ng{\"o}r, Suzan Uskudarli, Tunga G{\"u}ng{\"o}r

In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses.

Entity Linking Knowledge Base Population +6

Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages

no code implementations • 1 Jun 2017 • Onur Gungor, Eray Yildiz, Suzan Uskudarli, Tunga Gungor

We contribute by proposing several schemes for representing the morphological analysis of a word in the context of named entity recognition.

Morphological Analysis named-entity-recognition +2