Overview of content related to 'named entity recognition'
This page provides an overview of 3 articles related to 'named entity recognition', listing most recently updated content first. Note that filters may be applied to display a sub-set of articles in this category (see FAQs on filtering for usage tips). Select this link to remove all filters.
Named entity recognition (NER) (also known as entity identification and entity extraction) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. State-of-the-art NER systems for English produce near-human performance. (Excerpt from Wikipedia article: Named entity recognition)
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Top authorsAriadne contributors most frequently referring to 'named entity recognition':
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| Title | Article summary | Date |
|---|---|---|
Towards Interoperabilty of European Language Resources |
Sophia Ananiadou and colleagues describe an ambitious new initiative to accelerate Europe-wide language technology research, helped by their work on promoting interoperability of language resources. |
July 2011, issue67, feature article |
The National Centre for Text Mining: A Vision for the Future |
Sophia Ananiadou describes NaCTeM and the main scientific challenges it helps to solve together with issues related to deployment, use and uptake of NaCTeM's text mining tools and services. |
October 2007, issue53, feature article |
The National Centre for Text Mining: Aims and Objectives |
Sophia Ananiadou, Julia Chruszcz, John Keane, John McNaught and Paul Watry describe NaCTeM's plans to provide text mining services for UK academics. |
January 2005, issue42, feature article |
