Publication Type : Book Chapter
Publisher : Springer Verlag
Source : Studies in Computational Intelligence, Springer Verlag, Volume 823, p.341-370 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064620944&doi=10.1007%2f978-3-030-12500-4_20&partnerID=40&md5=3bf9f05dfdd2a65b70b6d41843595b0f
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Preposition sense disambiguation has huge significance in Natural language processing tasks such as Machine Translation. Transferring the various senses of a simple preposition in source language to a set of senses in target language has high complexity due to these many-to-many relationships, particularly in English-Malayalam machine translation. In order to reduce this complexity in the transfer of senses, in this paper, we used linguistic information such as noun class features and verb class features of the respective noun and verb correlated to the target simple preposition. The effect of these linguistic features for the proper classification of the senses (postposition in Malayalam) is studied with the help of several machine learning algorithms. The study showed that, the classification accuracy is higher when both verb and noun class features are taken into consideration. In linguistics, the major factor that decides the sense of the preposition is the noun in the prepositional phrase. The same trend was observed in the study when the training data contained only noun class features. i.e., noun class features dominates the verb class features. © Springer Nature Switzerland AG 2019.
Cite this Research Publication : B. Premjith, Dr. Soman K. P., Anand Kumar M., and Jyothi Ratnam D., “Embedding linguistic features in word embedding for preposition sense disambiguation in english—Malayalam machine translation context”, Studies in Computational Intelligence, vol. 823, pp. 341-370, 2019.