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    Wyświetlanie 1-9 z 9
    Tytuł:
    Multiaspect Text Categorization Problem Solving: a Nearest Neighbours Classifier Based Approaches and Beyond
    Autorzy:
    Zadrożny, S.
    Kacprzyk, J.
    Gajewski, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/950980.pdf
    Data publikacji:
    2015
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    text categorization
    intelligent system
    nearest neighbours classifiers
    topic tracking and detection
    fuzzy majority
    Opis:
    We deal with the problem of the multiaspect text categorization which calls for the classification of the documents with respect to two, in a sense, orthogonal sets of categories. We briefly define the problem, mainly referring to our previous work, and study the application of the k- nearest neighbours algorithm. We propose a new technique meant to enhance the effectiveness of this algorithm when applied to the problem in question. We show some experimental results confirming usefulness of the proposed approach.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2015, 9, 4; 58-70
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Feature selection using particle swarm optimization in text categorization
    Autorzy:
    Aghdam, M. H.
    Heidari, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/91792.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    classification system
    feature selection
    text categorization
    particle swarm optimization (PSO)
    system klasyfikacji
    wybór funkcji
    kategoryzacja tekstu
    optymalizacja rojem cząstek
    Opis:
    Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 231-238
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effective multi-label classification method with applications to text document categorization
    Autorzy:
    Glinka, K.
    Zakrzewska, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/94735.pdf
    Data publikacji:
    2016
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    multilabel classification
    text categorization
    problem transformation method
    text management
    Opis:
    Increasing number of repositories of online documents resulted in growing demand for automatic categorization algorithms. However, in many cases the texts should be assigned to more than one class. In the paper, new multi-label classification algorithm for short documents is considered. The presented problem transformation Labels Chain (LC) algorithm is based on relationship between labels, and consecutively uses result labels as new attributes in the following classification process. The method is validated by experiments conducted on several real text datasets of restaurant reviews, with different number of instances, taking into account such classifiers as kNN, Naive Bayes, SVM and C4.5. The obtained results showed the good performance of the LC method, comparing to the problem transformation methods like Binary Relevance and Label Powerset.
    Źródło:
    Information Systems in Management; 2016, 5, 1; 24-35
    2084-5537
    2544-1728
    Pojawia się w:
    Information Systems in Management
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Categorization of persons based on their mentions in Polish news texts
    Autorzy:
    Pachocki, Maciej
    Wróblewska, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/385228.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    fined‐grained named entity classification
    text classification
    categorization of persons
    Opis:
    Our goal described in this paper was to design, imple‐ ment and test a method of categorization of mentions of persons in Polish news texts. We gathered and classified the input data in order to measure the accuracy of the method. Train and test data were constructed by using lists of persons collected from YAGO knowledge base and Polish Wikipedia. During tests the efficiency of categori‐ zation depending on different representations of a per‐ son was studied. Experiments were executed on our and a chosen solution from literature. The results are shown and discussed in the paper.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 2; 42-49
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Biblical and Anthropocentric Phraseologisms in Dmytro Dontsov’s Works: A Cognitive Aspect
    Autorzy:
    Mykytyuk, Oksana
    Powiązania:
    https://bibliotekanauki.pl/articles/31341231.pdf
    Data publikacji:
    2022
    Wydawca:
    Wydawnictwo Uniwersytetu Śląskiego
    Tematy:
    Cognitive linguistics
    journalistic text
    partially-authorial / authorial phraseologism
    categorization
    phraseological-and-semantic microfield
    Dmytro Dontsov
    Opis:
    The paper deals with Dontsov’s “phraseological speech” in the framework of cognitive linguistics. The ways of creating partially-authorial (biblical) and authorial (anthropocentric) phraseologisms are discussed and their assignment to different phraseological-semantic microfields is suggested. A number of Dontsov’s phraseologisms are viewed as linguomental pictures of the world that are potentially acceptable for a wider use.Methodologically, the research presents a cluster of general scholarly methods and those used in cognitive linguistics as well as special approaches developed in modern anthropocentric research. Methods of cognitive linguistics are of the utmost importance and include categorizing the phenomena of the objective reality and the interdisciplinary method of interpretation related to the correlation of language data with cultural studies, political science, ethnopsychology and other disciplines. Semantic and contextual analyses are also used as supplementary methods. The potential value of the research is ensured by its contribution to the modern anthropocentric linguistics that aims at studying language through its speaker.Structural-and-logical scheme illustrating the cognitive stages of generating a phraseologism is suggested and the importance of categorization of lingual phenomena is emphasized. Dontsov’s phraseologisms are claimed to be means of exposure of the national Ukrainian lingual picture, symbols of the national worldview, and the prism of the world perception and understanding.
    Źródło:
    Neophilologica; 2022, 34; 1-16
    0208-5550
    2353-088X
    Pojawia się w:
    Neophilologica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of various GPU acceleration strategies in text categorization processing flow
    Autorzy:
    Korduła, Ł.
    Wielgosz, M.
    Karwatowski, M.
    Pietroń, M.
    Żurek, D.
    Wiatr, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/114132.pdf
    Data publikacji:
    2017
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    GPU
    NLP
    text categorization
    OpenCL
    Opis:
    Automatic text categorization presents many difficulties. Modern algorithms are getting better in extracting meaningful information from human language. However, they often significantly increase complexity of computations. This increased demand for computational capabilities can be facilitated by the usage of hardware accelerators like general purpose graphic cards. In this paper we present a full processing flow for document categorization system. Gram-Schmidt process signatures calculation up to 12 fold decrease in computing time of system components.
    Źródło:
    Measurement Automation Monitoring; 2017, 63, 6; 203-205
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of nature-inspired algorithms for text feature selection
    Autorzy:
    Çoban, Önder
    Powiązania:
    https://bibliotekanauki.pl/articles/27312909.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    nature-inspired algorithms
    feature selection
    text categorization
    Opis:
    This paper provides a comprehensive assessment of basic feature selection (FS) methods that have originated from nature-inspired (NI) meta-heuristics; two well-known filter-based FS methods are also included for comparison. The performances of the considered methods are compared on four balanced highdimensional and real-world text data sets regarding the accuracy, the number of selected features, and computation time. This study differs from existing studies in terms of the extent of experimental analyses that were performed under different circumstances where the classifier, feature model, and term-weighting scheme were different. The results of the extensive experiments indicated that basic NI algorithms produce slightly different results than filter-based methods for the text FS problem. However, filter-based methods often provide better results by using lower numbers of features and computation times.
    Źródło:
    Computer Science; 2022, 23 (2); 179--204
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of methods and means of text mining
    Autorzy:
    Rybchak, Z.
    Basystiuk, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/411072.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    text mining
    text analytics
    data analysis
    high-quality information
    text categorization
    text clustering
    document summarization
    sentiment analysis
    sieć językowa
    analiza tekstu
    analiza danych
    wysoka jakość informacji
    klasyfikacja tekstowa
    kategoryzacja tekstowa
    grupowanie tekstu
    streszczenie dokumentów tekstowych
    technika sentiment analysis
    Opis:
    In Big Data era when data volume doubled every year analyzing of all this data become really complicated task, so in this case text mining systems, techniques and tools become main instrument of analyzing tones and tones of information, selecting that information that suit the best for your needs and just help save your time for more interesting thing. The main aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in text mining.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 73-78
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Adapting text categorization for manifest based android malware detection
    Autorzy:
    Coban, Onder
    Ozel, Selma Ayse
    Powiązania:
    https://bibliotekanauki.pl/articles/305467.pdf
    Data publikacji:
    2019
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    Android
    malware detection
    text categorization
    machine learning
    Opis:
    Malware is a shorthand of malicious software that are created with the intent of damaging hardware systems, stealing data, and causing a mess to make money, protest something, or even make war between governments. Malware is often spread by downloading some applications for your hardware from some download platforms. It is highly probable to face with a malware while you try to load some applications for your smart phones nowadays. Therefore it is very important that some tools are needed to detect malware before loading them to the hardware systems. There are mainly three different approaches to detect malware: i) static, ii) dynamic, and iii) hybrid. Static approach analyzes the suspicious program without executing it. Dynamic approach, on the other hand, executes the program in a controlled environment and obtains information from operating system during runtime. Hybrid approach, as its name implies, is the combination of these two approaches. Although static approach may seem to have some disadvantages, it is highly preferred because of its lower cost. In this paper, our aim is to develop a static malware detection system by using text categorization techniques. To reach our goal, we apply text mining techniques like feature extraction by using bag-of-words, n-grams, etc. from manifest content of suspicious programs, then apply text classification methods to detect malware. Our experimental results revealed that our approach is capable of detecting malicious applications with an accuracy between 94.0% and 99.3%.
    Źródło:
    Computer Science; 2019, 20 (3); 305-327
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

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