Дисертації з теми "News classification"
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Kroha, Petr, and Ricardo Baeza-Yates. "Classification of Stock Exchange News." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401576.
Повний текст джерелаIn diesem Bericht untersuchen wir, wieviel Ähnlichkeit gute und schlechte Nachrichten im Kontext von Langzeitmarkttrends besitzen. Wir diskutieren die Verbindungen zwischen Text Mining, Klassifikation und Information Retrieval. Wir präsentieren Beispiele, die identische Wortmengen verwenden, aber trotzdem recht unterschiedliche Bedeutungen besitzen; Beispiele, die sowohl positiv als auch negativ interpretiert werden können. Sie zeigen Probleme auf, die mit Methoden des Information Retrieval nicht gelöst werden können. Um nach Gemeinsamkeiten in Nachrichtengruppen zu suchen, verwendeten wir Klassifikatoren (z.B. Naive Bayes), nachdem wir herausgefunden hatten, dass der Einsatz von diagnostizierenden Methoden keine vernünftigen Resultate erzielte. Für unsere Experimente nutzten wir historische Daten des Deutschen Aktienindex DAX 30
Sandsmark, Håkon. "Spoken Document Classification of Broadcast News." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19226.
Повний текст джерелаBlein, Florent. "Automatic Document Classification Applied to Swedish News." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-3065.
Повний текст джерелаThe first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN).
The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories.
This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories.
The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment.
The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.
Kolluru, BalaKrishna. "Broadcast news processing: Structural Classification, Summarisation and Evaluation." Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485892.
Повний текст джерелаRozman, Darija. "UDC in 2008 - Brief news from Slovenia." UDC Consortium, 2008. http://hdl.handle.net/10150/106214.
Повний текст джерелаKurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.
Повний текст джерелаSvärd, Mikael, and Philip Rumman. "COMBATING DISINFORMATION : Detecting fake news with linguistic models and classification algorithms." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209755.
Повний текст джерелаSyftet med denna studie är att undersöka möjligheten att på ett pålitligt sättskilja mellan fabricerade och autentiska nyheter med hjälp av Naive bayesalgoritmer,detta involverar en komparativ studie mellan två olika typer avalgoritmer. Arbetet innehåller även en översikt över hur lingvistisk textanalyskan användas för detektion och ett försök gjordes att extrahera information medhjälp av ordfrekvenser. Det förs även en diskussion kring hur de olika aktörernaoch parterna inom näringsliv och regeringar påverkas av och hur de hanterarbedrägeri kopplat till falska nyheter. Studien försöker vidare undersöka vilkasteg som kan tas mot en fungerande lösning för att motarbeta falska nyheter. Algoritmernagav i slutändan otillfredställande resultat och ordfrekvenserna kundeinte ensamma ge nog med information. De tycktes dock potentiellt användbarasom en del i ett större maskineri av algoritmer och strategier ämnade att hanteradesinformation.
Gravenhorst, Claus. "News media processing and interactive presentation." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16574.
Повний текст джерелаArevian, Garen Zohrab. "Recurrent neural networks for text classification of news articles from the Reuters Corpus." Thesis, University of Sunderland, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439972.
Повний текст джерелаLagerkrants, Eleonor, and Jesper Holmström. "Using machine learning to classify news articles." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-59449.
Повний текст джерелаLi, Chaoyang, and Ke Liu. "Smart Search Engine : A Design and Test of Intelligent Search of News with Classification." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37601.
Повний текст джерелаKan'an, Tarek Ghaze. "Arabic News Text Classification and Summarization: A Case of the Electronic Library Institute SeerQ (ELISQ)." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/74272.
Повний текст джерелаPh. D.
Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.
Повний текст джерелаRekathati, Faton. "Curating news sections in a historical Swedish news corpus." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166313.
Повний текст джерелаSimmler, Urs. "Simulation-News in Creo 1.0." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-68394.
Повний текст джерелаLuo, Ying. "A Credibility-based Classification of Journalistic Blogs:A Literature Study on Credibility Indicators and Examination of Illustrative Cases." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1237233585.
Повний текст джерелаRohleder, Rebekka. "Im/Possible Prisons: News from the Future of Work." Universität Leipzig, 2021. https://ul.qucosa.de/id/qucosa%3A73701.
Повний текст джерелаDeaville, James. "Selling War: Television News Music and the Shaping of American Public Opinion." Bärenreiter Verlag, 2012. https://slub.qucosa.de/id/qucosa%3A72045.
Повний текст джерелаWan, Zhibin, and Huatai Xu. "Performance comparison of different machine learningmodels in detecting fake news." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37576.
Повний текст джерелаBürger, Thomas. "Welcome to the SLUB - IFLA 2017 News Media Satellite Conferences." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16342.
Повний текст джерелаDogan, Ebru. "Content-based Audio Management And Retrieval System For News Broadcasts." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611018/index.pdf.
Повний текст джерелаBoss, Katherine, and Meredith Broussard. "Describing dynamic data journalism: developing a survey of news applications." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16654.
Повний текст джерелаVollbrecht, Ralf, and Verena Odrig. "Medienwelten - Zeitschrift für Medienpädagogik." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A70797.
Повний текст джерелаKlinger, Jessica, and Antje Müller. "Eignen sich Kindernachrichten für Kinder?" Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-196024.
Повний текст джерелаTV News programs for children, within the context of political socialization, are not only hugely important but are also being faced with constant challenges. The following study focuses on Germany’s Logo! and Britain’s Newsround, two of the larger scale television news programs on offer for children. The study offers ananalytical, quality comparison of the two shows. It looks both at the quality criteria of normative, context-related and formal assessment criteria, whilst exploring the perspective of the recipient, the producer and more scientific approaches. The study concludes with an analysis of the format preparation of the two shows and analyses their respective suitability for children. The study offers a profound insight into the similarities and differences between these two broadcasts and considers how we can attempt to measure the quality of children’s news programs
Hoffmann, Martin. "Informationskompetenz: Auszubildende werden „Fit for News“ - Pilotprojekt entwickelt Unterrichtsmodule für Sachsens Berufsschüler." Landkreis Nordsachsen, 2019. https://slub.qucosa.de/id/qucosa%3A34422.
Повний текст джерелаSimmler, Urs. "Simulation-News in Creo 1.0 & Creo 2.0." Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-87115.
Повний текст джерелаChuck, Lisa. "A PREDICTIVE MODEL FOR BENCHMARKING ACADEMIC PROGRAMS (PBAP)USING U.S. NEWS RANKING DATA FOR ENGINEERING COLLEGES OFFERING GRADU." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2397.
Повний текст джерелаEd.D.
Department of Educational Research, Technology and Leadership
Education
Educational Leadership
Miranda, Ackerman Eduardo Jacobo. "Extracting Causal Relations between News Topics from Distributed Sources." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-130066.
Повний текст джерелаDallmann, Christine, and Ralf Vollbrecht. "Editorial: Kindernachrichten." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-196014.
Повний текст джерелаRiedel, Ursula, Frank Richter, Uwe Huebner, Jan Wunderlich, Holger Trapp, Matthias Clauss, Karsten Baensch, et al. "Mitteilungen des URZ 1/2/1996." Universitätsbibliothek Chemnitz, 1996. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199600134.
Повний текст джерелаChuck, Lisa Gay Marie. "A Predictive Model for Benchmarking Academic Programs (pBAP) Using U.S. News Ranking Data for Engineering Colleges Offering Graduate Programs." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2396.
Повний текст джерелаEd.D.
Department of Educational Research, Technology and Leadership
Education
Educational Leadership
Hagen, Lutz M. "Relevance of News in the Digital Age: Journalism between Vanishing Business Models and an Unchained Public." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16402.
Повний текст джерелаSimmler, Urs. "Mechanism-News in PTC Creo." Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-141525.
Повний текст джерелаGlaser, Karen. "News from the pragmatics classroom: Contrasting the inductive and the deductive approach in the teaching of pragmatic competence." De Gruyter, 2016. https://ul.qucosa.de/id/qucosa%3A21364.
Повний текст джерелаLindblom, Rebecca. "News Value Modeling and Prediction using Textual Features and Machine Learning." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167062.
Повний текст джерелаFischer, Günther, Ludwig Wolf, Jens Wagner, Wolfgang Riedel, Steffen Brose, Ursula Riedel, Rolf Köbe, and Frank Richter. "Mitteilungen des URZ 4/1995." Universitätsbibliothek Chemnitz, 1995. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-199500125.
Повний текст джерелаSvensson, Pontus. "Automated Image Suggestions for News Articles : An Evaluation of Text and Image Representations in an Image Retrieval System." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166669.
Повний текст джерелаLin, Song-Hua, and 林頌華. "Automatic Classification of News Titles." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/35886847351662807519.
Повний текст джерелаWei, Huang Chung, and 黃仲瑋. "Automatic Classification of Medical News." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/98507899902441991263.
Повний текст джерела國立交通大學
資訊科學系
87
There is hundreds of medical news everyday. Can we find out an efficient method to collect these data? Moreover, the computer can feed news automatically, store it in the server, analyze the context of news, and then find out which assortment the specific news is. In other words, it is not necessary for doctors and nurses to spend time on searching medical news. They only have to run this program, and then the classified medical news will appear in front of them. In this paper, I introduce how improved search of keywords can replace the function of sentence analysis. Furthermore, I discuss the special methods of cutting sentences which can increase the accurateness of analysis. At last I will show you how to record each sentence''s information by multi-words and special comparison rules. All of them are presented by HTML showcase.
Lee, Yen-Lung, and 李儼倫. "Dictionary-based news category classification : using sports news as example." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/f6g5au.
Повний текст джерела淡江大學
資訊工程學系碩士班
104
Rapid and vigorous development of information network technology has resulted in the largest data repository. Collecting relevant information in such a large body of data is rather difficult for any user. This paper is aimed to help users to grasp key information in a short period of time. We observe that term frequency in a article can be used as keyword for that article. Article theme can be easily grasped based on these keywords. Therefore, users can find the information they want through keyword and significantly reduce unnecessary search time. Proper word segmentation enables article theme extraction. And article classification can be achieved by theme differentiation. We use 320 articles in the theme classification experiment. These articles are divided into two categories: training and testing. There are 285 training samples, all belonging to the sports news theme. There are 15 testing samples that are consists of themes picked at random. The result is able to pick out 6 articles which belonging to sport news theme among the 15 testing samples. Among the 20 negative samples, there are 4 false positives, all due to names related to sports events.
Shen, Shih-Yun, and 沈時宇. "Automatic Web News Classification and Subscription." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/56278137447557477758.
Повний текст джерела國立中正大學
資訊工程研究所
90
With the rapid growth of Internet, Web News becomes more and more popular. And many people have changed the habit of reading News, instead of reading News from newspapers or TV channel they read News from WWW now. But most News web sites are short of automated process, they need to invoke human effort to classify or select their News in the news page. Therefore, our system was built to solve these problems, and it can fetch News data, classifies and clusters News content automatically. Our system will reduce the human effort invoked and increase the efficiency of the process from getting the News to showing the result. In our thesis, we utilize the HTML format to make the result of the classification more accurate. We define the similarity between documents, and use the k-means algorithm to make the cluster process more efficient. Our system also provides two kinds of User Interfaces to make users reading News more efficiently and more conveniently.
Lin, Ta-Che, and 林大澈. "Automatic Classification System of News Pages." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/02316906563335323642.
Повний текст джерела淡江大學
資訊工程學系碩士班
98
There are more and more information in the Internet by the vigorous development of the Internet. But this rapid development has brought about a new problem. That is there are such a large number of news and information, and the classifications of all the news sites are not the same. How to quickly organize the data and absorb them is a need to face. In this paper, a classification system is set up through several researches focusing on the news page classification. It can automatically update the news pages and go on automatic classification. This system is based on Naïve Bayes Classifier. When it calculates the probability of news classification, it also calculates the weight of each word at the same time. Thus, it can increase the accuracy of classification and decrease the occurrence of a variety of classifications or not being classified (belong to all categories). This classification system has the basic ability to distinguish information after training module has trained 960 news. Afterwards, by testing 200 news, the system’s average recall rate can be 78%, achieving acceptable results. Experimental results show that this system is able to have good performance of classification in the way that the text in the news are divided into broken words by word thesaurus as a feature information of training module, go on to count the word frequency information, combining concept of the weight with Naïve Bayes Classifier.
Hsieh, Wei-Che, and 謝維哲. "A Classification Oriented News Summary Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/58132459684812522469.
Повний текст джерела中原大學
資訊管理研究所
97
The Internet has been applied more and more widely in recent years. Therefore, various information has been digitized to facilitate its spread on the Internet. With the development of digitization, huge amount of information has been created and it’s not as difficult for users to acquire information as before. As a result, it is important for users to exclude unnecessary information to get what they really need. Traditionally, the single-document summarization method has been used to present the single news summary. With the development of the news automatic clustering technology, most news portal sites also classify news, but they do not give different summaries to different news types. As a result, readers may not quickly find the news they care about or they may miss relative news by the search of this kind of news summaries. This research is to come out with a news summary method that combines with the classification-oriented technology. This method creates news summaries by the concept of information retrieval, the calculation of TF*IDF weight of the words, K-means clustering, and document summarization. We then define the top 10% words of the classified news by their weight as relevant words. In addition, since the headlines are usually the key points of the news, and the first sentence of the first paragraph is usually the main point and the last sentence of the last paragraph is usually the conclusion, this research also adjusts the weights of relevant words according to those concepts. The purpose of the research is to provide a method of classification-oriented news summary so that readers can get the main points of the news in a short time and determine whether the news is what they want.
Ming, Chien Chun, and 簡俊銘. "Text Classification Using the News Headlines." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/13721581226405198270.
Повний текст джерела華梵大學
資訊管理學系碩士班
102
With the increasing popularity of Internet, information dependence on the Internet also gives rise. How to use the electronic media to spread information quickly and efficiently obtain useful information on their needs, is an important issue. This study discussed the classification using the news headlines. The automatic document classification (text classification) method was adopted. The word processing and the frequency information of words were used to extract the attributes (features) for the classification of the news articles using the Weka data mining system. We discussed the impacts of the ratio of the files (1:1, 1:5, 1:10) and the numbers of attributes (128, 256, 512, 1024, 2048) for the classification of news. The classifications using the contents and the headlines of the news articles were also compared. The experimental results show that the classification of the news content gets the accuracy of 96.3636%, and slightly higher than the correct rate of 93.6364% for the headlines. The difference is 2.0454%. The results show that the proposed method can be used for the classification of the real-time electronic news using the title field of the news. Keywords: text classification, news headlines, data mining
Tien, Kao-Ming, and 田高銘. "Empirical Study of News Sentiment Classification: Evidence from Anue Financial News." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9zndbp.
Повний текст джерела國立中山大學
財務管理學系研究所
107
Nowadays, online news has become one of the judgments for investors to make investment decisions. However, a large amount of information generated by financial news websites everyday makes investors unable to use traditional human reading and screening methods to judge and verify the current market sentiment reflected by each news report. In order to help investors understand the current market sentiment quickly, we use the techniques of text mining and text classification to classify new sentiments. This study collects Taiwanese stock market news of Anue Financial News and use different methods of text pre-processing and classifier to achieve the best classification performance. The empirical results show:(1) N-gram feature extraction can improve the accuracy of all classifiers, especially the naive Bayes classifier which can effectively overcome shortcomings of the independence assumptions. (2) TF-IDF feature selection only effective for naive Bayes classifier. Under the circumstances of the number of words decreasing, it can improve the accuracy and reduce the training time. (3) The Chi-square test and mutual information feature selection can improve the accuracy of both fastText and Multi-layer Perceptron. Furthermore, the combination of Chi-square test feature and fastText achieved the best performance in this study.
Lu, Wen-Jane, and 呂文蓁. "Finding a Suitable Hierarchical Classification for News." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/11208911446932580156.
Повний текст джерела國立成功大學
資訊管理研究所
96
Online news has became a trend, manual text classification distributes text documents into one or more pre-defined categories of similar documents. It is essential to develop an automatic classification method to reduce manual work. Currently, the news is a hierarchical structure. We wonder if a classification method applicable to each level. In this paper, we first collected most popular classification methods. Several suitable combinations are selected and applied to different hierarchical level. Unlike other papers, they all apply one method in all level to classify news. We use three most popular text classification algorithms, Support Vector Machine, Naïve Bayes and K-Nearest-Neighbor, to classify Reuter Corpus, Volume 1. We expect to find a better combinatorial classification method to improve classification accuracy and performance.
Rodrigues, João Filipe Carriço. "Fake news classification in European Portuguese language." Master's thesis, 2020. http://hdl.handle.net/10071/22194.
Повний текст джерелаUm pouco por todo o mundo foram tomadas várias iniciativas para combater fake news. Muitos governos (França, Alemanha, Reino Unido e Espanha, por exemplo), à sua maneira, começaram a tomar medidas relativamente à responsabilidade legal para aqueles que fabricam ou propagam notícias falsas. Foram feitas algumas mudanças estruturais nos meios de comunicação sociais, a fim de avaliar as notícias em geral. Muitas equipas foram construídas inteiramente para combater fake news, mais especificamente, os denominados "fact-checkers". Essas equipas têm vindo a adotar diferentes tipos de técnicas para realizar as suas tarefas: desde o uso dos jornalistas para descobrir a verdade por detrás de uma declaração controversa, até aos cientistas de dados, que através de técnicas mais avançadas como as técnicas de Text Minning e métodos de classificação de Machine Learning, apoiam as decisões dos jornalistas. Muitas das entidades que visam manter ou aumentar a sua reputação, começaram a concentrar-se em elevados padrões de qualidade e informação fiável, o que levou à criação de departamentos oficiais e dedicados de verificação de factos. Na primeira parte deste trabalho, contextualizamos o Português Europeu no âmbito da detecção e classificação de notícias falsas, fazendo um levantamento do seu actual estado da arte. De seguida, apresentamos uma solução end-to-end que permite facilmente extrair e armazenar notícias portuguesas europeias previamente classificadas. Utilizando os dados extraídos aplicámos algumas das técnicas de Text Minning e de Machine Learning mais utilizadas, apresentadas na literatura, a fim de compreender e avaliar as possíveis limitações dessas técnicas, neste contexto em específico
Yu, Tung-lin, and 余東霖. "Two-phase Classification Approach for Identifying News Category." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71868724106650851383.
Повний текст джерела國立中央大學
資訊管理研究所
98
The news classification problem is concerned with how to assign the correct category for the unclassified news. Although a large number of past studies have studied this problem, a common weakness of these studied is that their classification algorithms were usually designed from technical perspective and they seldom considered how experts really classify the news in a practical classification process. In this research, we first observe how media workers classify news in their daily operations, and we find that their classification process mainly consists of the following operations. (1) If some important keywords or phrases are present in the news, then they directly assign the news to certain categories. (2) Otherwise, they must check in details the whole content of news to determine which category it should belong to. (3) Since a news category may contain several independent but related subcategories, the news is usually classified by assigning it to the most appropriate subcategory, which can in turn determine its category. By imitating the above working process, we proposed a news classification algorithm. In the learning phase, we use associative classification rules to find representative keywords in each category. In addition, we further generate a number of subcategories by clustering news under each category. In the classification phase, we assign unclassified news the most appropriate category by using associative classification rules if rules’ confidence is high enough. Otherwise, we will determine the category by measuring the similarity between unclassified news and subcategories. The experimental comparison shows that our approach has better and more stable classification performance than traditional algorithms.
Che-Min, Chen. "A Cross-Trainging Approach for Bilingual Web News Classification." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611330653.
Повний текст джерелаWang, Zhi-Hong, and 王誌鴻. "Developing Ontological Mechanisms for Chinese News Analysis and Classification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/76675601423260626884.
Повний текст джерела中原大學
資訊管理研究所
95
The common ways of context analysis have been limited to human understanding of vocabularies, speech judgment and synonym mapping, resulting in a lack of understanding of the deeper implications of the content of the text. Based on an ontology knowledge classification structure, our research aims to analyze news semantics and classify news scenarios. The integration of a scenario analysis mechanism into the knowledge structure would allow for different readings of news under different scenarios, benefiting classification of information. In this research we collected relevant knowledge element properties, attributes and any existing classifying structures first. Following Formal Concept Analysis (FCA), we then integrate the elements and dominant/recessive attributes analyzed by the experts into a concept plan which shows the relationship among all the elements, their properties, and classification structures. To enhance the analysis of news contents from an information level to a semantic level, this research utilizes a two-step process, Resource Description Framework (RDF) and Web Ontology Language (OWL); the former improves the expression of vocabularies and the latter adds descriptive logic to help express knowledge under different scenarios. We used the “IC Components” of the electronics industry as a case study to collect the knowledge the experts have regarding the different scenarios the manufacturers encounter. The knowledge was then used to analyze the Chinese news headlines based on the mechanism of ontology and establish a semantics classification as affected by different scenarios afterwards, which will be used as empirical application.