<?xml version="1.0" encoding="UTF-8"?><feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
<title>Machine Learning Methods for Named Entity Recognition with Limited Resource</title>
<link href="http://127.0.0.1/xmlui/handle/123456789/656" rel="alternate"/>
<subtitle/>
<id>http://127.0.0.1/xmlui/handle/123456789/656</id>
<updated>2026-04-17T05:35:25Z</updated>
<dc:date>2026-04-17T05:35:25Z</dc:date>
<entry>
<title>Machine Learning Methods for Named Entity Recognition with Limited Resource</title>
<link href="http://127.0.0.1/xmlui/handle/123456789/657" rel="alternate"/>
<author>
<name>Saha, Sujan Kumar</name>
</author>
<id>http://127.0.0.1/xmlui/handle/123456789/657</id>
<updated>2015-06-01T11:07:00Z</updated>
<published>2010-01-01T00:00:00Z</published>
<summary type="text">Machine Learning Methods for Named Entity Recognition with Limited Resource
Saha, Sujan Kumar
A named entity (NE) denotes a noun or noun phrase referring to a name belonging to a&#13;
predefined category like person, location and organization. The task of identifying and&#13;
categorizing named entities from text is known as named entity recognition (NER). NEs&#13;
are often the pivotal as well as the most information-bearing elements of a text, and NER&#13;
systems find application in a number of tasks like information extraction, text mining&#13;
and machine translation.
</summary>
<dc:date>2010-01-01T00:00:00Z</dc:date>
</entry>
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