ADDRESSING NOISE ISSUES IN SENTENCE STRUCTURE FOR MORPHOLOGICAL ANALYSIS OF ENGLISH LANGUAGE SENTENCES FOR HINDI LANGUAGE USERS
Abstract
Morphological analysis is a fundamental task in natural language processing that involves breaking down words into their constituent morphemes to understand their grammatical structure and meaning. However, when morphological analysis is applied to English language sentences by Hindi language users, noise issues arise due to the syntactic and structural differences between the two languages. This study addresses these noise issues and proposes techniques to improve the accuracy of morphological analysis for English sentences processed by Hindi language users. By exploring methods to handle word order variations, phrasal differences, and other syntactic disparities, this research aims to enhance the effectiveness of morphological analysis tools for bilingual users and facilitate their comprehension of English language sentences.
Keywords
Morphological analysis, noise issues, sentence structureHow to Cite
References
R. Mahesh, K. Sinha, and A. Thaku, “How to get best results out of a machine translation system: A case study of English to Hindi Translation,” CSI Journal, vol. 38, no. 4, Oct.-Dec. 2008.
L. V. Subramaniam, S. Roy, T. A. Faruquie, and S. Negi, “A survey of types of text noise and techniques to handle noisy text,” in Proc. The Third Workshop on Analytics for Noisy Unstructured Text Data, ACM, 2009, pp. 115-122.
TDIL. Machine translation. Indian Language Technology Proliferation and Deployment Center. [Online]. Available: http://tdil-dc.in/components/com_mtsystem/CommonUI/homeMT.php
H. Trost, X2MORF: A morphological component based on augmented two-level morphology, Research Report, 1991.
James, Natural Language Understanding, 2nd ed. Pearson Education, 2008.
H. Schmid, “A Programming language for finite state transducers,” in Proc. the 5th International Workshop on Finite State Methods in Natural Language Processing, Helsinki, Finland, July 13, 2005, pp. 308-314.
H. Schmid. Developing computational morphologies with the SFST tools. Tutorial SFST Tool. [Online]. Available: http://www.cis.uni-muenchen.de/~schmid/tools/SFST/data/SFST-Tutorial.pdf
R. M. K. Sinha and A. Jain, “Angla Hindi: an English to Hindi machine-aided translation system,” MT Summit IX, New Orleans, USA, pp. 494-497, 2003.
D. Jurafsky and J. H. Martin, Speech and Language Processing, 2nd ed. Prentice Hall Inc., 2002.
License
Copyright (c) 2023 Richa Mehta
This work is licensed under a Creative Commons Attribution 4.0 International License.
