Identification of Character Adjectives from Mahabharata

Apurba Paul, Dipankar Das


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.
Anthology ID:
R17-1074
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
569–576
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_074
DOI:
10.26615/978-954-452-049-6_074
Bibkey:
Cite (ACL):
Apurba Paul and Dipankar Das. 2017. Identification of Character Adjectives from Mahabharata. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 569–576, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Identification of Character Adjectives from Mahabharata (Paul & Das, RANLP 2017)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_074