TAILIEUCHUNG - THEORY AND APPLICATIONS FOR ADVANCED TEXT MINING

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe. | THEORY AND APPLICATIONS FOR ADVANCED TEXT MINING Edited by Shigeaki Sakurai Theory and Applications for Advanced Text Mining http 31 15 Edited by Shigeaki Sakurai Contributors Hiep Luong Susan Gauch Qiang Wang Alessio Leoncini Fabio Sangiacomo Paolo Gastaldo Rodolfo Zunino Hanmin Jung Sa-Kwang Song Seungwoo Lee Sung-Pil Choi Fadoua Ataa Allah Siham Boulaknadel Masaomi Kimura Hidenao Abe Jose Luis Oliveira Sérgio Matos David Campos Hidetsugu Nanba Aya Ishino Toshiyuki Takezawa Alan Porter Yi Zhang Published by InTech Janeza Trdine 9 51000 Rijeka Croatia Copyright 2012 InTech All chapters are Open Access distributed under the Creative Commons Attribution license which allows users to download copy and build upon published articles even for commercial purposes as long as the author and publisher are properly credited which ensures maximum dissemination and a wider impact of our publications. After this work has been published by InTech authors have the right to republish it in whole or part in any publication of which they are the author and to make other personal use of the work. Any republication referencing or personal use of the work must explicitly identify the original source. Notice Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials instructions methods or ideas contained in the book. Publishing Process Manager Iva Lipovic Technical Editor InTech DTP team Cover InTech Design team First published November 2012 Printed in Croatia A free online edition of this book is available at Additional hard copies can be obtained from orders@ Theory and Applications for Advanced Text Mining Edited by .

• Kỹ thuật lập trình
• Relation Extraction
• Technical Intelligence
• Web Mining
• Lexical Expansion
• Mining Techniques
• Amazigh Language
• Luận án Tiến sĩ
• Công nghệ thông tin
• Semantic relation extraction
• Khoa học máy tính
• Advanced deep learning models
• Computer science
• Shortest dependency path
• BMC Bioinformatics
• Predicate argument structures
• Biomedical relation extraction
• Open information extraction
• Fundamental technology
• Reducing Wrong Labels
• Distant Supervision
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• A Graph based Cross lingual Projection Approach
• Weakly Supervised Relation Extraction
• Gary Geunbae Lee
• Pattern Learning
• Hierarchical Topic Model
• Temporally Anchored Relation Extraction
• Guillermo Garrido
• Anselmo Penas
• End to End Relation Extraction
• External Semantic Repositories
• An Error Analysis
• Social Media Documents
• labeled data
• xử lý ngôn ngữ tự nhiên
• Multi Task Transfer Learning
• Jing Jiang
• Unsupervised Relation Extraction
• Mining Wikipedia Texts
• Information from the Web
• Semi supervised Relation Extraction
• Large scale Word Clustering
• Ang Sun
• Exploiting Syntactico Semantic Structures
• Yee Seng Chan
• Composite Kernels
• Frank Reichartz Hannes Korte Gerhard Paass Fraunhofer
• The Tradeoffs Between Open
• Traditional Relation Extraction
• Michele Banko and Oren Etzioni
• A Novel Feature based Approach to Chinese
• Entity Relation Extraction
• Long Papers
• Modeling Commonality
• Related Classes in Relation Extraction
• Zhou GuoDong Su Jian Zhang Min
• mô hình ngôn ngữ
• Label Propagation Based Semi supervised Learning
• Jinxiu Chen
• Exploring Correlation
• Dependency Relation Paths
• Answer Extraction
• Using Corpus Statistics on Entities
• Improve Semi supervised Relation
• Extraction from the Web
• Instance Splitting Strategies
• Dependency Relation based
• Information Extraction
• an Unsupervised Web Relation
• Extraction System
• Benjamin Rosenfeld
• Exploring Various Knowledge
• ZHOU GuoDong
• Simple Algorithms
• Complex Relation Extraction
• Applications to Biomedical IE
• Dependency Tree Kernels
• Aron Culotta