TY - JOUR
T1 - Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes
AU - Mohamed, Muhidin
AU - Oussalah, Mourad
N1 - This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
PY - 2014/7/1
Y1 - 2014/7/1
N2 - An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system’s outstanding performance in comparison to three different state-of-the-art systems.
AB - An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system’s outstanding performance in comparison to three different state-of-the-art systems.
UR - https://thesai.org/Publications/ViewPaper?Volume=5&Issue=7&Code=ijacsa&SerialNo=25
U2 - 10.14569/IJACSA.2014.050725
DO - 10.14569/IJACSA.2014.050725
M3 - Article
SN - 2158-107X
VL - 5
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 7
ER -