Thesis On Text Classification

Doctor 39;s Thesis Text Categorization using Machine Learning . Text Categorization using Machine Learning. Hirotoshi Taira. February 5, 2002. Department of Information Processing. Graduate School of Information Science. Nara Institute of Science and Technology nbsp; Thesis Automatic Text Categorization of documents in the High . Automatic Text Categorization of documents in the High Energy Physics domain. Dr. Luis Alfonso Ure na-López (supervisor). Dr. Ralf Steinberger (supervisor). Arturo Montejo-Ráez (author). 15 December, 2005 nbsp; Using Unlabeled Data to Improve Text Classification - Kamal Nigam . Classification. Kamal Paul Nigam. May 2001. CMU-CS-01-126. School of Computer Science. Carnegie Mellon University. Pittsburgh, PA 15213. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Thesis Committee: Tom M. Mitchell, Chair. A Comparative study on text categorization - Semantic Scholar is a supervised learning task, defined as assigning category labels to new documents based on likelihood suggested by a training set of labeled documents. Two examples of methodology for text categorizations are Naive Bayes and K-Nearest. Neighbor. In this thesis, we implement two nbsp; Chapter 6 A SURVEY OF TEXT CLASSIFICATION - algorithms. Keywords: MINING TEXT DATA is used to predict a class label for this instance. In the hard version of the classification problem, a particular label is explicitly assigned to the instance the classification. The main thesis is that documents. Text classification of short messages - Lund University Publications Text classification of short messages. Anton Lundborg. MASTER 39;S THESIS LUND UNIVERSITY 2017. Department of Computer Science. Faculty of Engineering LTH. ISSN 1650-2884. LU-CS-EX 2017-14 nbsp; Keyword based Text Categorization . First and foremost, my greatest thanks go to Dr. Ido Dagan for introducing me to the wonderful world of Natural Language Processing, and for supervising this research. His constant support, thorough guidance, and nbsp; Efficient Text Categorization with a Large Number of Classes with a Large Number of Categories. Rayid Ghani. Advisor: Tom Mitchell. Text Categorization. Numerous Applications. Search Engines/Portals; Customer Service; Email Routing . Domains: Topics; Genres; Languages. Problems. Practical applications such as nbsp; Natural Language Processing and Automated Text Categorization tion/Answering and Text Summarization should take advantage from category information as it helps to select the domain knowledge that language applications usually use in their processing. In this thesis, a study of the interaction between Natural Language Process- ing and Text Categorization has been nbsp; Learning Features for Text Classification Significant performance improvements are observed in multiple conversational text classification tasks. This thesis also proposes feature affinity and cluster regularization, which uses feature relationships learned from unlabeled data to regularize training. This regularization scheme converts supervised nbsp;

Advanced Text Analytics and Machine Learning Approach for

Advanced Text Analytics and Machine Learning Approach for. Document Classification. A Thesis. Submitted to the Graduate Faculty of the. University of New Orleans. In partial fulfillment of the. Requirements for the degree of. Master of Science in. Computer Science by. Chaitanya Anne. Bachelor of nbsp; Automatic web page categorization using text - DiVA portal This master 39;s thesis is an exploratory study, with the goal to try and answer the question of how can machine learning and natural language processing tools for text classification be used to perform automatic web page categorization? Machine learning and natural language processing methods and tools nbsp; Text Classification and Layout Analysis for Document Reassembling and Layout Analysis for Document Reassembling. DISSERTATION submitted in partial fulfillment of the requirements for the degree of. Doktor der technischen Wissenschaften by. Markus Diem. Registration Number 0226595 to the Faculty of Informatics at the Vienna University of Technology. Advisor: Ao. N-grams for Text Classification Using Supervised Machine Learning Using. Supervised Machine Learning. Algorithms. Kennedy Odhiambo Ogada. A thesis submitted in fulfillment for the degree of Doctor of Philosophy In. Information Technology in the Jomo. Kenyatta University Of Agriculture and. Technology. 2016 nbsp; Inter-class relationships in text classification - entitled Inter-class relationships in text classification by Shantanu Godbole is approved for the degree of DOCTOR OF. PHILOSOPHY. Examiners. Supervisors. Chairman. Date: 7 December 2006. Place: IIT Bombay nbsp; Modeling Non-Standard Text Classification Tasks - Bauhaus deals with technologies for automatic text classification based on machine learning and aims to improve the effectiveness of classifying unseen texts. The focus is on non-standard text classification tasks. We call a task non-standard if the classification goes beyond the texts subjects. using background knowledge to improve text classification by Sarah Zelikovitz. Dissertation Director: Haym Hirsh. Automatic text categorizers use a corpus of labeled textual strings or documents to assign the correct label to previously unseen strings or documents. Often the given set of labeled examples, or training set nbsp; Supervised Text Classification of Medical Triage Reports - Formal of. Medical Triage Reports. Author: Jurgen Kleverwal, BSc. Supervisors: Dr. ir. Dolf Trieschnigg. Dr. Pim van den Broek. Bram Kievit, MSc. Michiel Hakvoort, BSc. A thesis submitted in fulfillment of the requirements for the degree of Master of Science in the. Formal Methods and Tools Group. Automatic Ticket Triage Using Supervised Text Classification - IS MU including the background of the models and tech- niques we use in our evaluation. In the third chapter, we establish the methodology of our work. The fourth chapter is the core of the thesis, it includes the evaluation of the models and analysis of the datasets. In the second to last chapter, we discuss the nbsp; tuomo nieminen text classification using bag of words - Tampereen The goal of this thesis work was to implement a method in classifying short text- based features into 27 business sectors. The original data was noisy and con- tained words in multiple languages, whereas the classification was intended to work in English. The data was filtered using NLTK libraries and nbsp; Improving Methods for Single-label Text Categorization - CiteSeerX includes a comprehensive comparison between the classifi- cation methods that are most frequently used in the Text Categorization area and the combinations of methods proposed.

Thesis - School of Electrical Engineering and Computer Science

task and discusses it effectiveness. The thesis concludes with Chapter 5, which contains a summary and suggested directions for nbsp; OPTIMIZATION OF TEXT CLASSIFICATION - Thapar University USING SUPERVISED AND. UNSUPERVISED LEARNING APPROACH. Thesis submitted in partial fulfillment of the requirements for the award of degree of. Master of Engineering in. Computer Science and Engineering. Submitted By. Suresh Kumar. (Roll No. 851232009). MASTER THESIS IN SOFTWARE CONSTRUCTION - AAU classification process has been chosen. This master thesis uses a part of the Enron e-mail collection 1 for training and testing phase. The best result achieved combination of single classifiers with F-measure equal to 0. 7102. The topics elaborated in the thesis, both the text and the software part, offer to the. Large-scale Multi-Label Text Classification for an Online - Helda for an Online News Monitoring System. Computer Science. November 24, 2015. 77 pages 0 appendices multi-label learning, text categorization, information extraction, NLP, online business news. This thesis provides a detailed exploration of numerous methods some nbsp; Using Information Extraction and Text Classification - BIBSYS Brage . We will present theory about systematic literature reviews, give a short introduction into informa- tion retrieval, navigate through information extraction and text classification, . PhD Thesis of Andrea Addis categorization, which is the field of study of this thesis work. 2. 1 Introduction. The world has widely changed in terms of communicating, acquiring and storing in-. Improving Multi-class Text Classification with Naive Bayes by Jason Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification. Thesis Supervisor: Tommi Jaakkola. Dimensionality Reduction Techniques for Enhancing Automatic Text by. Dina Adel Said. A Thesis Submitted To the. Faculty of Engineering at Cairo University in Partial Fulfillment of the. Requirements for the Degree of. MASTER OF SCIENCE in. COMPUTER ENGINEERING. Leveraging Lexical-Semantic Knowledge for Text Classification Tasks This dissertation is concerned with the applicability of knowledge, contained in lexical-semantic resources, to text classification tasks. Lexical-semantic resources aim at systematically encoding various types of information about the meaning of words and their relations. Text classification is the task of nbsp;