Developing Multilingual Text Mining Workflows in UIMA and U-Compare

2012 ◽  
pp. 82-93 ◽  
Author(s):  
Georgios Kontonasios ◽  
Ioannis Korkontzelos ◽  
Sophia Ananiadou
Keyword(s):  
Text Mining ◽  
Multilingual Text

scholarly journals Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

2012 ◽  
Vol 35 (1) ◽  
pp. 87-109 ◽  
Author(s):  
César de Pablo-Sánchez ◽  
Isabel Segura-Bedmar ◽  
Paloma Martínez ◽  
Ana Iglesias-Maqueda
Keyword(s):  
Text Mining ◽  
Named Entities ◽  
Linguistic Patterns ◽  
Multilingual Text

A multilingual text mining approach to web cross-lingual text retrieval

2004 ◽  
Vol 17 (5-6) ◽  
pp. 219-227 ◽  
Author(s):  
Rowena Chau ◽  
Chung-Hsing Yeh
Keyword(s):  
Text Mining ◽  
Text Retrieval ◽  
Multilingual Text ◽  
Cross Lingual

Multilingual Text Mining

2011 ◽  
pp. 1380-1385 ◽  
Author(s):  
Peter A. Chew
Keyword(s):  
Text Mining ◽  
Search Engines ◽  
Text Processing ◽  
The European Union ◽  
End User ◽  
The World ◽  
Recent Developments ◽  
English Speaking ◽  
Multilingual Text ◽  
Multiple Languages

The principles of text mining are fundamental to technology in everyday use. The world wide web (WWW) has in many senses driven research in text mining, and with the growth of the WWW, applications of text mining (like search engines) have by now become commonplace. In a way that was not true even less than a decade ago, it is taken for granted that the ‘needle in the haystack’ can quickly be found among large volumes of text. In most cases, however, users still expect search engines to return results in the same language as that of the query, perhaps the language best understood by the user, or the language in which text is most likely to be available. The distribution of languages on the WWW does not match the distribution of languages spoken in general by the world’s population. For example, while English is spoken by under 10% of the world’s population (Gordon 2005), it is still predominant on the WWW, accounting for perhaps two-thirds of documents. There are variety of possible reasons for this disparity, including technological inequities between different parts of the world and the fact that the WWW had its genesis in an English-speaking country. Whatever the cause for the dominance of English, the fact that two-thirds of the WWW is in one language is, in all likelihood, a major reason that the concept of multilingual text mining is still relatively new. Until recently, there simply has not been a significant and widespread need for multilingual text mining. A number of recent developments have begun to change the situation, however. Perhaps these developments can be grouped under the general rubric of ‘globalization’. They include the increasing adoption, use, and popularization of the WWW in non-Englishspeaking societies; the trend towards political integration of diverse linguistic communities (highly evident, for example, in the European Union); and a growing interest in understanding social, technological and political developments in other parts of the world. All these developments contribute to a greater demand for multilingual text processing – essentially, methods for handling, managing, and comparing documents in multiple languages, some of which may not even be known to the end user.

A method for multilingual text mining and retrieval using growing hierarchical self-organizing maps

2008 ◽  
Vol 35 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Hsin-Chang Yang ◽  
Chung-Hong Lee ◽  
Ding-Wen Chen
Keyword(s):  
Text Mining ◽  
Self Organizing Maps ◽  
Multilingual Text ◽  
Self Organizing
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