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Öğe Association rule based acquisition of hyponym and hypernym relation from a Turkish corpus(2012) Yildiz, T.; Yildirim, S.In this paper, we propose a method for the automatic acquisition of hypernym/hyponymy relations from a Turkish raw text. Once the model has extracted prospective hyponyms by using lexico-syntactic patterns, an Apriori algorithm is applied to eliminate faulty hyponyms and increase precision. We show that a model based on a particular lexico-syntactic pattern and association rules for Turkish language can successfully retrieve many is-a relation with high precision. © 2012 IEEE.Öğe Corpus-driven hyponym acquisition for Turkish language(2012) Yildirim, S.; Yildiz, T.In this study, we propose a method for acquisition of hyponymy relations for the Turkish Language. This integrated method relies on both lexico-syntactic pattern and semantic similarity. Once the model has extracted the items using patterns it applies similarity based elimination of the incorrect ones in order to increase precision. We show that the algorithm based on a particular lexico-syntactic pattern for Turkish language can retrieve many hyponymy relations and also demonstrate that elimination based on semantic similarity gives promising results. We discuss how we measure the similarity between the concepts. The objective is to get better relevance and more precise results. The experiments show that this approach gives successful results with high precision. © 2012 Springer-Verlag.Öğe Extraction of part-whole relations from Turkish corpora(2013) Yildiz, T.; Yildirim, S.; Diri, B.In this work, we present a model for semi-automatically extracting part-whole relations from a Turkish raw text. The model takes a list of manually prepared seeds to induce syntactic patterns and estimates their reliabilities. It then captures the variations of part-whole candidates from the corpus. To get precise meronymic relationships, the candidates are ranked and selected according to their reliability scores. We use and compare some metrics to evaluate the strength of association between a pattern and matched pairs. We conclude with a discussion of the result and show that the model presented here gives promising results for Turkish text. © 2013 Springer-Verlag.Öğe Sentiment Analysis through Transfer Learning for Turkish Language(Institute of Electrical and Electronics Engineers Inc., 2019) Akin, S.E.; Yildiz, T.Sentiment Analysis (SA) has received much attention in recent years. In this paper, we proposed a model based on the transfer learning technique to address SA problem. First, we utilize word embeddings that are trained on 322K documents from Turkish Wikipedia. The model employs a regular Long Short-Term Memory (LSTM) with dropout. Secondly, we fine-tuned the pre-trained language model on two different target datasets (restaurant and product reviews) independently. Finally, the LSTM is trained to classify reviews according to positive and negative sentiments and its associated performance is assessed. This study is also considered to be the important attempt that uses transfer learning by applying a fine-tuning technique and deep learning architecture to address SA problem for Turkish Language. © 2019 IEEE.
