Improving the Annotations in the Turkish Universal Dependency Treebank

no code implementations • WS 2019 • Utku T{\"u}rk, Furkan Atmaca, {\c{S}}aziye Bet{\"u}l {\"O}zate{\c{s}}, Balk{\i}z {\"O}zt{\"u}rk Ba{\c{s}}aran, Tunga G{\"u}ng{\"o}r, Arzucan {\"O}zg{\"u}r

A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages

1 code implementation • CONLL 2018 • {\c{S}}aziye Bet{\"u}l {\"O}zate{\c{s}}, Arzucan {\"O}zg{\"u}r, Tunga G{\"u}ng{\"o}r, Balk{\i}z {\"O}zt{\"u}rk

We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences.

Dependency Parsing Word Embeddings

Edition 1.1 of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions

no code implementations • COLING 2018 • Carlos Ramisch, Silvio Ricardo Cordeiro, Agata Savary, Veronika Vincze, Verginica Barbu Mititelu, Archna Bhatia, Maja Buljan, C, Marie ito, Polona Gantar, Voula Giouli, Tunga G{\"u}ng{\"o}r, Abdelati Hawwari, Uxoa I{\~n}urrieta, Jolanta Kovalevskait{\.e}, Simon Krek, Timm Lichte, Chaya Liebeskind, Johanna Monti, Carla Parra Escart{\'\i}n, Behrang Qasemizadeh, Renata Ramisch, Nathan Schneider, Ivelina Stoyanova, Ashwini Vaidya, Abigail Walsh

Corpora were created for 20 languages, which are also briefly discussed.

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

Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification

1 code implementation • COLING 2018 • G{\"o}zde Berk, Berna Erden, Tunga G{\"u}ng{\"o}r

To the best of our knowledge, this paper is the first one that employs the bidirectional LSTM-CRF model for VMWE identification.