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Öğe ANN-Based Estimation of Groundwater Quality Using a Wireless Water Quality Network(Hindawi Ltd, 2014) Kilicaslan, Yilmaz; Tuna, Gurkan; Gezer, Gulsum; Gulez, Kayhan; Arkoc, Orhan; Potirakis, Stelios M.Water is essential for life. Considering its importance for humans, it must be periodically analyzed to ensure its quality. In this study, a wireless water quality network is deployed to collect water quality parameters periodically and an artificial neural network-based estimation method is proposed to estimate groundwater quality. Estimating groundwater quality enables the authorities to take immediate actions for ensuring water quality. Compared to traditional water quality analysis methods, the proposed method has the advantage of letting the authorities know the quality of their water resources beforehand. A set of simulation studies given in this paper proves the efficiency and accuracy of the proposed method.Öğe A Computational Model for Resolving Pronominal Anaphora in Turkish Using Hobbs' Naive Algorithm(World Acad Sci, Eng & Tech-Waset, 2005) Tuefeki, Pinar; Kilicaslan, YilmazIn this paper we present a computational model for pronominal anaphora resolution in Turkish. The model is based on Hobbs' Naive Algorithm [4, 5, 6], which exploits only the surface syntax of sentences in a given text.Öğe An effective and efficient Web content extractor for optimizing the crawling process(Wiley, 2014) Uzun, Erdinc; Guener, Edip Serdar; Kilicaslan, Yilmaz; Yerlikaya, Tarik; Agun, Hayri VolkanClassical Web crawlers make use of only hyperlink information in the crawling process. However, focused crawlers are intended to download only Web pages that are relevant to a given topic by utilizing word information before downloading the Web page. But, Web pages contain additional information that can be useful for the crawling process. We have developed a crawler, iCrawler (intelligent crawler), the backbone of which is a Web content extractor that automatically pulls content out of seven different blocks: menus, links, main texts, headlines, summaries, additional necessaries, and unnecessary texts from Web pages. The extraction process consists of two steps, which invoke each other to obtain information from the blocks. The first step learns which HTML tags refer to which blocks using the decision tree learning algorithm. Being guided by numerous sources of information, the crawler becomes considerably effective. It achieved a relatively high accuracy of 96.37% in our experiments of block extraction. In the second step, the crawler extracts content from the blocks using string matching functions. These functions along with the mapping between tags and blocks learned in the first step provide iCrawler with considerable time and storage efficiency. More specifically, iCrawler performs 14 times faster in the second step than in the first step. Furthermore, iCrawler significantly decreases storage costs by 57.10% when compared with the texts obtained through classical HTML stripping. Copyright (c) 2013 John Wiley & Sons, Ltd.Öğe Learning-based pronoun resolution for Turkish with a comparative evaluation(Academic Press Ltd- Elsevier Science Ltd, 2009) Kilicaslan, Yilmaz; Guner, Edip Serdar; Yildirim, SavasThe aim of this paper is twofold. On the one hand, it attempts to explore several machine learning models for pronoun resolution in Turkish, a language not sufficiently studied with respect to anaphora resolution and rarely being subjected to machine learning experiments. On the other hand, this paper offers an evaluation of the classification performances of the learning models in order to gain insight into the question of how to match a model to the task at hand. In addition to the expected observation that each model should be tuned to an optimum level of expressive power so as to avoid underfitting and overfitting, the results also suggest that non-linear models properly tuned to avoid overfitting outperform linear ones when applied to the data used in our experiments. (C) 2008 Elsevier Ltd. All rights reserved.Öğe An NLP-based 3D scene generation system for children with autism or mental retardation(Springer-Verlag Berlin, 2008) Kilicaslan, Yilmaz; Ucar, Ozlem; Guner, Edip SerdarIt is well-known that people with autism or mental retardation experience crucial problems in thinking and communicating using linguistic structures. Thus, we foresee the emergence of text-to-image conversion systems to let such people establish a bridge between linguistic expressions and the concepts these expressions refer to via relevant images. S2S is such a system for converting Turkish sentences into representative 3D scenes via the mediation of an HPSG-based NLP module. A precursor to S2S, a non-3D version, has been tested with a group of students with autism and mental retardation in a special education center and has provided promising results motivating the work presented in this paper.Öğe AN NLP-BASED APPROACH FOR IMPROVING HUMAN-ROBOT INTERACTION(Sciendo, 2013) Kilicaslan, Yilmaz; Tuna, GurkanThis study aims to explore the possibility of improving human- robot interaction (HRI) by exploiting natural language resources and using natural language processing (NLP) methods. The theoretical basis of the study rests on the claim that effective and efficient human robot interaction requires linguistic and ontological agreement. A further claim is that the required ontology is implicitly present in the lexical and grammatical structure of natural language. The paper offers some NLP techniques to uncover (fragments of) the ontology hidden in natural language and to generate semantic representations of natural language sentences using that ontology. The paper also presents the implementation details of an NLP module capable of parsing English and Turkish along with an overview of the architecture of a robotic interface that makes use of this module for expressing the spatial motions of objects observed by a robot.Öğe Pronoun Resolution in Turkish Using Decision Tree and Rule-Based Learning Algorithms(Springer-Verlag Berlin, 2009) Yildirim, Savas; Kilicaslan, Yilmaz; Yildiz, TugbaThis paper reports on the results of some pronoun resolution experiments performed by applying a decision tree and a rule-based algorithm on an annotated Turkish text. The text has been compiled mostly from various popular child stories in a semi-automatic way. A knowledge-lean learning model has been devised using only nine most commonly employed features. An evaluation and comparison of the performances achieved with the two different algorithms is offered in terms of the recall, precision and f-measure metrics.Öğe Visualization of Turkish for Autistic and Mentally Retarded Children(IEEE, 2008) Kilicaslan, Yilmaz; Ucar, Ozlem; Ucar, Erdem; Guner, Edip SerdarThe use of software technologies for supporting the education of disabled children continues to increase both in quantity and quality. L2I is a set of computer programs that assists the education and training of autistic and mentally retarded children. These children are known to have difficulties in grasping fairly abstract concepts. L2I is intended to circumvent the abstractness of linguistically encoded conceptual structures via visual images. The software has also the potential to teach and/or assist a variety of basic skills including reading, writing and communicating. Each component constituting L2I has been tested with a group of students with autism and mental retardation in a special education center. In the light of the observations made over these tests, this paper presents how L2I can be benefited from in a special education program.
