N-gram and Gazetteer List Based Named Entity Recognition for Urdu: A Scarce Resourced Language
Faryal Jahangir, Waqas Anwar, Usama Ijaz Bajwa, Xuan Wang · 2012
Extraction of named entities (NEs) from the text is an important operation in many natural language processing applications like information extraction, question answering, machine translation etc. Since early 1990s the researchers have taken greater interest in this field and a lot of work has been done regarding Named Entity Recognition (NER) in different languages of the world. Unfortunately Urdu language which is a scarce resourced language has not been taken into account. In this paper we present a statistical Named Entity Recognition (NER) system for Urdu language using two basic n-gram models, namely unigram and bigram. We have also made use of gazetteer lists with both techniques as well as some smoothing techniques with bigram NER tagger. This NER system is capable to recognize 5 classes of NEs using a training data containing 2313 NEs and test data containing 104 NEs. The unigram NER Tagger using gazetteer lists achieves up to 65.21 % precision, 88.63 % recall and 75.14 % f-measure. While the bigram NER Tagger using gazetteer lists and Backoff smoothing achieves up to 66.20 % precision, 88.18% recall and 75.83 f-measure.