Search Results for author: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

Found 28 papers, 0 papers with code

Cross-lingual morphological inflection with explicit alignment

no code implementations WS 2019 {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

This paper describes two related systems for cross-lingual morphological inflection for SIGMORPHON 2019 Shared Task participation.

Cross-Lingual Transfer Morphological Inflection

Neural and Linear Pipeline Approaches to Cross-lingual Morphological Analysis

no code implementations WS 2019 {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Jeremy Barnes

This paper describes T{\"u}bingen-Oslo team{'}s participation in the cross-lingual morphological analysis task in the VarDial 2019 evaluation campaign.

Morphological Analysis

Phonetic Vector Representations for Sound Sequence Alignment

no code implementations WS 2018 Pavel Sofroniev, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

This study explores a number of data-driven vector representations of the IPA-encoded sound segments for the purpose of sound sequence alignment.

Identifying Depression on Reddit: The Effect of Training Data

no code implementations WS 2018 Inna Pirina, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

This paper presents a set of classification experiments for identifying depression in posts gathered from social media platforms.

General Classification

Fewer features perform well at Native Language Identification task

no code implementations WS 2017 Taraka Rama, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

In the speech track, an LDA classifier based only on i-vectors performed better than a combination system using text features from speech transcriptions and i-vectors.

Native Language Identification

Discriminating Similar Languages with Linear SVMs and Neural Networks

no code implementations WS 2016 {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama

This paper describes the systems we experimented with for participating in the discriminating between similar languages (DSL) shared task 2016.

Language Identification

LSTM Autoencoders for Dialect Analysis

no code implementations WS 2016 Taraka Rama, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

Computational approaches for dialectometry employed Levenshtein distance to compute an aggregate similarity between two dialects belonging to a single language group.

Dimensionality Reduction

Universal Dependencies for Turkish

no code implementations COLING 2016 Umut Sulubacak, Memduh Gokirmak, Francis Tyers, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Joakim Nivre, G{\"u}l{\c{s}}en Eryi{\u{g}}it

The Universal Dependencies (UD) project was conceived after the substantial recent interest in unifying annotation schemes across languages.

A set of open source tools for Turkish natural language processing

no code implementations LREC 2014 {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin

This paper introduces a set of freely available, open-source tools for Turkish that are built around TRmorph, a morphological analyzer introduced earlier in Coltekin (2010).

Lemmatization Morphological Disambiguation

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