KNN TFIDF Based Named Entity Recognition
B.Upendra
, A.Sudheer Babu
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.
"KNN TFIDF Based Named Entity Recognition", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.1, Issue 12, page no.35 - 39, December-2016, Available :https://ijsdr.org/papers/IJSDR1612008.pdf
Volume 1
Issue 12,
December-2016
Pages : 35 - 39
Paper Reg. ID: IJSDR_160963
Published Paper Id: IJSDR1612008
Downloads: 000347038
Research Area: Engineering
Country: KRISHNA, ANDHRA PRADESH, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
