@InProceedings{paul-das:2017:RANLP,
  author    = {Paul, Apurba  and  Das, Dipankar},
  title     = {Identification of Character Adjectives from Mahabharata},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {569--576},
  abstract  = {The present paper describes the identification of prominent characters and
	their adjectives from Indian mythological epic, Mahabharata, written in English
	texts. However, in contrast to the tra-ditional approaches of named entity
	identifica-tion, the present system extracts hidden attributes associated with
	each of the characters (e.g., character adjectives). We observed distinct
	phrase level linguistic patterns that hint the pres-ence of characters in
	different text spans. Such six patterns were used in order to extract the
	cha-racters. On the other hand, a distinguishing set of novel features (e.g.,
	multi-word expression, nodes and paths of parse tree, immediate ancestors etc.)
	was employed. Further, the correlation of the features is also measured in
	order to identify the important features. Finally, we applied various machine
	learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision
	Tree, Random Forest etc.) along with deep learning to classify the patterns as
	characters or non-characters in order to achieve decent accuracy. Evaluation
	shows that phrase level linguistic patterns as well as the adopted features are
	highly active in capturing characters and their adjectives.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_074}
}