Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/13791
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dc.contributor.authorPremjith B.
dc.contributor.authorSoman K.P.
dc.contributor.authorAnand Kumar M.
dc.contributor.authorJyothi Ratnam D.
dc.date.accessioned2020-03-31T14:15:22Z-
dc.date.available2020-03-31T14:15:22Z-
dc.date.issued2019
dc.identifier.citationStudies in Computational Intelligence, 2019, Vol.823, pp.341-370en_US
dc.identifier.uri10.1007/978-3-030-12500-4_20
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/13791-
dc.description.abstractPreposition 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.en_US
dc.titleEmbedding linguistic features in word embedding for preposition sense disambiguation in english—Malayalam machine translation contexten_US
dc.typeBook Chapteren_US
Appears in Collections:3. Book Chapters

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