Computational Approach for Improving Fluency in Bangla NLG
door Das, Sumit
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The primary goal of Natural Language Generation (NLG) is to generate complete and fluent natural language text from underlying machine representation of information. The completeness of a text depends on its semantic correctness and quality of being understood. The fluency of a text depends on its grammatical correctness, coherence and naturalness.The fluency of a text generated is as important as its completeness. In NLG, prenominal modifier ordering and syntactic aggregation are two useful methods for improving the generated text fluency. Correct ordering of prenominal modifiers, modifying the same head noun, improves the fluency and naturalness of text. In syntactic aggregation simple text spans are combined by using linguistic rules. Unnecessary repeating words are deleted by this process thereby improving the fluency, coherence and conciseness of the text. In this thesis, we have presented computational approaches for these two problems in Bangla NLG.
Sumit Das completed his B.E. degree in CSE from Jadavpur University, Kolkata, India in 2006. He did M.S. in CSE from IIT Kharagpur, India in 2011. Currently he is working as a text mining researcher at Abzooba India Infotech Pvt. Ltd., Kolkata, India. His research interests include Text Mining, Natural Language Processing, and Assistive Technology.
LAP Lambert Academic Publishing
0.22 x 0.15 x 0.007 m; 0.213 kg