**Meet the editor**

Shigeaki Sakurai received BS degree in mathematics, MS degree in mathematics, and Ph.D. degree in engineering from Tokyo University of Science, in 1989, 1991, and 2001, respectively. He became a Professional Engineer of the Information Engineering field in 2004. He is currently with the Business Intelligence Laboratory, Advanced IT Laboratory, Toshiba Solutions Corporation

from Knowledge Media Laboratory, Corporate Research & Development Center, Toshiba Corporation. He has been a visiting professor at Tokyo Institute of Technology since 2009. He is a director of Japan Society for Fuzzy Theory and Intelligent Informatics. His research interests include data mining, computational intelligence, and web intelligence.

Contents

**Preface VII**

Hidenao Abe

Zunino

Chapter 1 **Survey on Kernel-Based Relation Extraction 1**

**Patterns of Terms in Documents 37**

Chapter 3 **Text Clumping for Technical Intelligence 51** Alan L. Porter and Yi Zhang

**Segmentation in Web Mining 75**

**and Text Mining 101**

Hanmin Jung, Sung-Pil Choi, Seungwoo Lee and Sa-Kwang Song

Alessio Leoncini, Fabio Sangiacomo, Paolo Gastaldo and Rodolfo

Chapter 2 **Analysis for Finding Innovative Concepts Based on Temporal**

Chapter 4 **A Semantic-Based Framework for Summarization and Page**

Chapter 5 **Ontology Learning Using Word Net Lexical Expansion**

Hiep Luong, Susan Gauch and Qiang Wang

Hidetsugu Nanba, Aya Ishino and Toshiyuki Takezawa

Chapter 7 **Analyses on Text Data Related to the Safety of Drug Use Based**

David Campos, Sérgio Matos and José Luís Oliveira

Chapter 6 **Automatic Compilation of Travel Information**

**on Text Mining Techniques 153**

Chapter 8 **Biomedical Named Entity Recognition: A Survey of Machine-Learning Tools 175**

**from Texts: A Survey 135**

Masaomi Kimura

## Contents

#### **Preface XI**


#### Chapter 9 **Toward Computational Processing of Less Resourced Languages: Primarily Experiments for Moroccan Amazigh Language 197** Fadoua Ataa Allah and Siham Boulaknadel

Preface

disadvantages.

mining techniques. It is composed of 9 chapters.

extracted temporal patterns is evaluated.

and present case results based on each process.

morphology ontology show the validity of acquired ontology.

from the benchmark data set.

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 includes 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 are developed, the text mining research field continues to expand for needs arising from various application fields. This book introduces representative works based on the latest trend of advanced text

In Chapter 1, Sung-Pil Choi et al. survey methods that extract relations between keywords (or key phrases) described in texts. This chapter focuses on the kernel-based relation extraction methods and introduces some representative methods. They are compared based on their characteristics and performance. This chapter summarizes their advantages and

In Chapter 2, Hidenao Abe focuses on a method that extracts temporal patterns of terms from temporally published documents. The method is composed of automatic term extraction, term importance indices, and temporal clustering. Medical research documents related to migraine drug therapy are analyzed based on the method. The validity of

In Chapter 3, Alan L. Porter et al. introduce a stepwise method that compiles lists of informative terms and phrases. The method includes field selection, removal of stop words and common terms, term manipulation, cleaning and removal of noise terms, and term consolidation. They apply it to Science, Technology and Innovative (ST&I) information sets

In Chapter 4, Alessio Leoncini et al. tackle on two issues which are required for the analysis of web pages. One is text summarization and the other is page segmentation. Semantic networks are introduced for these issues. They map natural language into an abstract representation. Their effectiveness is shown by applying them to the topic extraction task

In Chapter 5, Hiep Luong et al. present an acquisition method of domain-specific ontology. The method uses two key techniques. One is lexical expansion based on WordNet. It extracts new vocabulary words from data sources. The other is text mining from domainspecific literature. It enriches concepts of the words. Experimental results of an amphibian
