Juhi Tandon
Kohli Center on Intelligent Systems (KCIS), International Institute of Information Technology, Hyderabad (IIIT-H), Gachibowli, Hyderabad, India
Dipti Misra Sharma
Kohli Center on Intelligent Systems (KCIS), International Institute of Information Technology, Hyderabad (IIIT-H), Gachibowli, Hyderabad, India
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Published in: Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017), September 18-20, 2017, Università di Pisa, Italy
Linköping Electronic Conference Proceedings 139:29, p. 255-265
Published: 2017-09-13
ISBN: 978-91-7685-467-9
ISSN: 1650-3686 (print), 1650-3740 (online)
This paper presents our work to apply non linear neural network for parsing five resource poor Indian Languages belonging to two major language families- Indo-Aryan and Dravidian. Bengali and Marathi are Indo-Aryan languages whereas Kannada, Telugu and Malayalam belong to the Dravidian family. While little work has been done previously on Bengali and Telugu linear transition-based parsing, we present one of the first parsers for Marathi, Kannada and Malayalam. All the Indian languages are free word order and range from being moderate to very rich in morphology. Therefore in this work we propose the usage of linguistically motivated morphological features (suffix and postposition ) in the non linear framework, to capture the intricacies of both the language families. We also capture chunk and gender, number, person information elegantly in this model. We put forward ways to represent these features cost effectively
