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A Comprehensive Analysis of Static Word Embeddings for Turkish
Karahan Sarıtaş, Cahid Arda Öz, Tunga Güngör
Word embeddings are fixed-length, dense and distributed word representations that are used in natural language processing (NLP) applications. There are basically two types of word…
Hierarchical Multitask Learning Approach for BERT
Çağla Aksoy, Alper Ahmetoğlu, Tunga Güngör
Recent works show that learning contextualized embeddings for words is beneficial for downstream tasks. BERT is one successful example of this approach. It learns embeddings by sol…
Data and Representation for Turkish Natural Language Inference
Emrah Budur, Rıza Özçelik, Tunga Güngör +1
Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in…
Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
Onur Güngör, Suzan Üsküdarlı, Tunga Güngör
Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity rec…
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages
Onur Gungor, Eray Yildiz, Suzan Uskudarli +1
In this work, we present new state-of-the-art results of 93.59,% and 79.59,% for Turkish and Czech named entity recognition based on the model of (Lample et al., 2016). We contribu…