scholarly journals Development of Text Mining Tools for Information Retrieval from Patents

2017 ◽  
pp. 66-73 ◽  
Author(s):  
Tiago Alves ◽  
Rúben Rodrigues ◽  
Hugo Costa ◽  
Miguel Rocha
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Mining Tools

Information Analysis in Digital Library Environments

2013 ◽  
pp. 87-92
Author(s):  
Lauren Harrison
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Digital Library ◽  
Digital Libraries ◽  
Semantic Content ◽  
Direct Access ◽  
Pipeline Pilot ◽  
Analysis Of Results ◽  
Mining Tools

This chapter addresses the question of how the analysis of results retrieved from online bibliographic information systems changed over the last 32 years as digital libraries have evolved. It demonstrates that Digital Libraries of the future will enable knowledge discovery by providing direct access to the semantic content of documents through the implementation of text mining tools. To achieve this research with IR systems and text-mining tools, pipeline pilot (Bandy, et al., 2009), I2E (Vellay, 2009), and BioText will need to be conducted by experts in information retrieval not just subject scientific specialists.

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

Large Scale Text Mining Approaches for Information Retrieval and Extraction

2013 ◽  
pp. 3-45 ◽  
Author(s):  
Patrice Bellot ◽  
Ludovic Bonnefoy ◽  
Vincent Bouvier ◽  
Frédéric Duvert ◽  
Young-Min Kim
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Large Scale

Incorporating Text OLAP in Business Intelligence

2011 ◽  
pp. 77-101 ◽  
Author(s):  
Byung-Kwon Park ◽  
Il-Yeol Song
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Business Intelligence ◽  
Multidimensional Analysis ◽  
Web Pages ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Platform Architecture ◽  
Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

Events Automatic Extraction from Arabic Texts

2020 ◽  
pp. 1686-1704
Author(s):  
Emna Hkiri ◽  
Souheyl Mallat ◽  
Mounir Zrigui
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Text Mining ◽  
Machine Translation ◽  
Language Processing ◽  
Question Answering ◽  
Arabic Language ◽  
Event Extraction ◽  
Mining Machine ◽  
Open Domain

The event extraction task consists in determining and classifying events within an open-domain text. It is very new for the Arabic language, whereas it attained its maturity for some languages such as English and French. Events extraction was also proved to help Natural Language Processing tasks such as Information Retrieval and Question Answering, text mining, machine translation etc… to obtain a higher performance. In this article, we present an ongoing effort to build a system for event extraction from Arabic texts using Gate platform and other tools.

Text Mining

2008 ◽  
pp. 592-603 ◽  
Author(s):  
Antonina Durfee
Keyword(s):  
Text Mining ◽  
Deception Detection ◽  
Text Summarization ◽  
Authorship Attribution ◽  
Venture Capitalists ◽  
Help Desk ◽  
News Agencies ◽  
Textual Databases ◽  
Available Information ◽  
Mining Tools

Massive quantities of information continue accumulating at about 1.5 billion gigabytes per year in numerous repositories held at news agencies, at libraries, on corporate intranets, on personal computers, and on the Web. A large portion of all available information exists in the form of text. Researchers, analysts, editors, venture capitalists, lawyers, help desk specialists, and even students are faced with text analysis challenges. Text mining tools aim at discovering knowledge from textual databases by isolating key bits of information from large amounts of text, identifying relationships among documents. Text mining technology is used for plagiarism and authorship attribution, text summarization and retrieval, and deception detection.

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