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A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators

A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators

O. Isaac Osesina, John Talburt
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 17
ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781466611078|DOI: 10.4018/jbir.2012010104
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MLA

Osesina, O. Isaac, and John Talburt. "A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators." IJBIR vol.3, no.1 2012: pp.55-71. http://doi.org/10.4018/jbir.2012010104

APA

Osesina, O. I. & Talburt, J. (2012). A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators. International Journal of Business Intelligence Research (IJBIR), 3(1), 55-71. http://doi.org/10.4018/jbir.2012010104

Chicago

Osesina, O. Isaac, and John Talburt. "A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators," International Journal of Business Intelligence Research (IJBIR) 3, no.1: 55-71. http://doi.org/10.4018/jbir.2012010104

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Abstract

Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject domain making it applicable within different organizations. It departs from the natural language and machine learning methods in that it leverages the wide availability of huge amounts of data as well as high-performance computing to provide a data-intensive solution. Also, it does not rely on external resources such as dictionaries and gazettes for the language or domain knowledge.

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