no code implementations • LREC 2020 • Murathan Kurfal{\i}, Robert {\"O}stling, Johan Sjons, Mats Wir{\'e}n
We present a new set of 96 Swedish multi-word expressions annotated with degree of (non-)compositionality.
no code implementations • WS 2019 • Murathan Kurfal{\i}, Robert {\"O}stling
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation.
no code implementations • WS 2019 • Murathan Kurfal{\i}, Robert {\"O}stling
We present a very simple method for parallel text cleaning of low-resource languages, based on projection of word embeddings trained on large monolingual corpora in high-resource languages.
no code implementations • WS 2018 • Ahmet {\"U}st{\"u}n, Murathan Kurfal{\i}, Burcu Can
The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.
no code implementations • WS 2017 • Deniz Zeyrek, Murathan Kurfal{\i}
This paper presents the recent developments on Turkish Discourse Bank (TDB).
no code implementations • LREC 2016 • Elif Ahsen Acar, Deniz Zeyrek, Murathan Kurfal{\i}, Cem Boz{\c{s}}ahin
We conclude that it is possible to deduce AoA information for high frequency words with the corpus-based approach.