@article{1449,
  author = {Muazzam Ahmed Siddiqui, Mostafa El-Sayed Saleh, Abobakr Ahmed Bagais},
  title = {Extraction and Visualization of the Chain of Narrators from Hadiths using Named Entity Recognition and Classification},
  journal = {International Journal of Computational Linguistics Research},
  year = {2014},
  volume = {5},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jcl/fulltext/v5n1/2.pdf},
  abstract = {A Hadith is a report of the deeds or sayings of the prophet Muhammad. Each of these reports were orally transmitted from one person to another till it reached a person who recorded the report along with the chain of transmission. We present a system to automatically extract the chain of narrators from a Hadith through Named Entity Recognition and Classification, and present these transmission chains as a network. In a Hadith, the name of a person may appear as a narrator or as someone who is mentioned in the Hadith. This distinction of names is important as identifying and evaluating the narrators is an important part of Hadith studies. We manually annotated a large Hadith corpus with named entities and used a set of keywords and special verbs to train machine learning algorithms for named entity recognition and classification. The keywords and special verbs identified the context surrounding the tokens labeled as named entities. We compared the performance of different classifiers including generative (Naïve Bayes), and discriminative (K-nearest neighbour and decision tree) and were able to achieve a 90% precision and 82% recall for the named entities. The classifiers were evaluated on a different corpus within the same domain that resulted in an 80% precision and 73% recall. The best classifier was used to label a bigger Hadith corpus and the narrators names thus identified from each Hadith were concatenated to create a chain of narration from the Hadith. These chains were represented as a graph of narrators in the end.},
}