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International Journal of Scientific Development and Research - IJSDR
(An International Open Access Journal)
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2455-2631


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Published Paper Details
Paper Title: KNN TFIDF Based Named Entity Recognition
Authors Name: B.Upendra , A.Sudheer Babu
Unique Id: IJSDR1612008
Published In: Volume 1 Issue 12, December-2016
Abstract: The increasing volume of generated crime information readily available on the web makes the process of retrieving and analyzing and use of the valuable information in such texts manually a very difficult task. This work is focus on designing models for extracting specific information from the Web. Thus, this paper proposes an ensemble framework for named entity recognition task. The main aim is to efficiently integrating feature sets and classification algorithms to synthesize a more accurate classification procedure. First, three well-known text classification algorithms, namely K-Means and K-Nearest Neighbor classifiers, are employed as base-classifiers for each of the feature sets. Second, weighted voting ensemble method is used to combine theses three classifiers. Experimental results demonstrate that using ensemble model is an effective way to combine different feature sets and classification algorithms for better classification performance.
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Cite Article: "KNN TFIDF Based Named Entity Recognition", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.1, Issue 12, page no.35 - 39, December-2016, Available :http://www.ijsdr.org/papers/IJSDR1612008.pdf
Downloads: 00016029
Publication Details: Published Paper ID: IJSDR1612008
Registration ID:160963
Published In: Volume 1 Issue 12, December-2016
DOI (Digital Object Identifier):
Page No: 35 - 39
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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