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Full-Text Articles in Physical Sciences and Mathematics
Exploring Feature Sets For Turkish Word Sense Disambiguation, Bahar İlgen, Eşref Adali, Ahmet Cüneyd Tantuğ
Exploring Feature Sets For Turkish Word Sense Disambiguation, Bahar İlgen, Eşref Adali, Ahmet Cüneyd Tantuğ
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents an exploration and evaluation of a diverse set of features that influence word-sense disambiguation (WSD) performance. WSD has the potential to improve many natural language processing (NLP) tasks as being one of the most crucial steps in the area. It is known that exploiting effective features and removing redundant ones help improving the results. There are two groups of feature sets to disambiguate senses and select the most appropriate ones among a set of candidates: collocational and bag-of-words (BoW) features. We introduce the effects of using these two feature sets on the Turkish Lexical Sample Dataset (TLSD), …
Feature Selection For Movie Recommendation, Zehra Çataltepe, Mahi̇ye Uluyağmur, Esengül Tayfur
Feature Selection For Movie Recommendation, Zehra Çataltepe, Mahi̇ye Uluyağmur, Esengül Tayfur
Turkish Journal of Electrical Engineering and Computer Sciences
TV users have an abundance of different movies they could choose from, and with the quantity and quality of data available both on user behavior and content, better recommenders are possible. In this paper, we evaluate and combine different content-based and collaborative recommendation methods for a Turkish movie recommendation system. Our recommendation methods can make use of user behavior, different types of content features, and other users' behavior to predict movie ratings. We gather different types of data on movies, such as the description, actors, directors, year, and genre. We use natural language processing methods to convert the Turkish movie …
Predicting Acute Hypotensive Episode By Using Hybrid Features And A Neuro-Fuzzy Network, Marzieh Abbasinia, Fardad Farokhi, Shahram Javadi
Predicting Acute Hypotensive Episode By Using Hybrid Features And A Neuro-Fuzzy Network, Marzieh Abbasinia, Fardad Farokhi, Shahram Javadi
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents an approach for acute hypotensive episode (AHE) time series forecasting based on hybrid feature space and a neuro-fuzzy network. Prediction was accomplished through a combination of time domain and wavelet features by using six vital time series of each patient, obtained from MIMIC-II and available in the context of the Physionet-Computers in Cardiology 2009 Challenge. At first, statistical time domain features were used and then the wavelet coefficient was utilized for extracting time scale features. Further UTA feature selection was applied and 30 effective features were determined and achieved to predict AHE with 96.30 accuracy 1.5 h …