Sentence Generator for English Language using Formal Semantics

##plugins.themes.academic_pro.article.sidebar##

Published Feb 15, 2023
https://doi.org/10.47164/ijngc.v14i1.1090
Download
Requires Subscription pdf
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 14, Special Issue 1, February 2023

##plugins.themes.academic_pro.article.main##

Ankita Gore
a:1:{s:5:"en_US";s:61:"Shri Ramdeobaba College of engineering and Management, Nagpur";}
Vanshika Bajaj
Preeti Yadav
Vaishnavi Chouhan
Madhuri Tayal
M. Sasi Kumar

Abstract

Natural Language Processing (NLP), is more specifically the branch of ”artificial intelligence” (AI) concerned with providing computers the ability to comprehend spoken and written language in a manner similar to that of humans. It is used for practical purposes to help connects us with everyday activities such as texting, emailing, and cross-language communication. The requirement for intelligent systems that can read a text and listen to voice memos and can converse with people in a natural language like English has substantially increased in recent years. In this paper, the random clausal sentence generator which is simple, compound, and complex sentences are described. This random sentence generation is beneficial for students studying on online platforms to learn clauses as they will get a variety of exercises to practice. Initially, simple sentences get generated and subsequently moved on to compound sentence and complex sentence generation. In this method, roughly hundred
verbs are used to get varied randomness along with 3-4 conjunctions and objects which nearly fit with the verbs and give a syntactically and semantically meaningful sentence as the outcome.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Gore, A., Bajaj, V., Yadav, P., Chouhan, V., Tayal, M., & Kumar, M. S. (2023). Sentence Generator for English Language using Formal Semantics. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1090
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Bharati, A., Chaitanya, V., Sangal, R., and Ramakrishnamacharyulu, K. V. 1996.Natural language processing: a paninian perspective.
  2. Galitsky, B. A. and Kuznetsov, S. O. 2013. A web mining tool for assistance with creative writing. In Advances in Information Retrieval, P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. R¨uger, E. Agichtein, I. Segalovich, and E. Yilmaz, Eds. Springer Berlin Heidelberg, Berlin, Heidelberg, 828–831.
  3. Kulkarni, A. and Pai, M. 2019. Sanskrit sentence generator. In Proceedings of the 6th International Sanskrit Computational Linguistics Symposium. Association for Computational
  4. Linguistics, IIT Kharagpur, India, 1–13.
  5. Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., and Jurafsky, D. 2017. Adversarial learning for neural dialogue generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 2157–2169. DOI: https://doi.org/10.18653/v1/D17-1230
  6. Li, Y., Pan, Q., Wang, S., Yang, T., and Cambria, E. 2018a. A generative model for category text generation.
  7. Li, Y., Pan, Q., Wang, S., Yang, T., and Cambria, E. 2018b. A generative model for category text generation. DOI: https://doi.org/10.1016/j.ins.2018.03.050
  8. Lin, K., Li, D., He, X., Zhang, Z., and Sun, M.-t. 2017. Adversarial ranking for language generation. In Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Vol. 30. Curran Associates, Inc.
  9. Manome, K., Yoshikawa, M., Yanaka, H., Mart´ınez-Gomez, P. , Mineshima, K., and Bekki, D. 2018. Neural sentence generation from formal semantics. In Proceedings of the 11th International Conference on Natural Language Generation. Association for Computational Linguistics, Tilburg University, The Netherlands, 408–414. DOI: https://doi.org/10.18653/v1/W18-6549
  10. Martin, W. . 2018. High school English grammar composition. Regular ed. New Delhi: Blackie Elt Books an imprint of S.Chand Publishing pp.160-167.
  11. Nallapati, R., Zhou, B., dos Santos, C., Gulc¸ehre, C¸ ., and Xiang, B. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Association for DOI: https://doi.org/10.18653/v1/K16-1028
  12. Computational Linguistics, Berlin, Germany, 280–290.
  13. Perera, R. and Nand, P. 2017. Recent advances in natural language generation: A survey and classification of the empirical literature. COMPUTING AND INFORMATICS 36, 1(May), 1–32. DOI: https://doi.org/10.4149/cai_2017_1_1