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Reseach Article

Generating Weather Forecast Texts with Case based Reasoning

by Ibrahim Adeyanju
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 10
Year of Publication: 2012
Authors: Ibrahim Adeyanju

Ibrahim Adeyanju . Generating Weather Forecast Texts with Case based Reasoning. International Journal of Computer Applications. 45, 10 ( May 2012), 35-40. DOI=10.5120/6819-9176

@article{ 10.5120/6819-9176,
author = { Ibrahim Adeyanju },
title = { Generating Weather Forecast Texts with Case based Reasoning },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 10 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-40 },
numpages = {9},
url = { },
doi = { 10.5120/6819-9176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:37:18.633590+05:30
%A Ibrahim Adeyanju
%T Generating Weather Forecast Texts with Case based Reasoning
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 10
%P 35-40
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

Several techniques have been used to generate weather forecast texts. In this paper, case based reasoning (CBR) is proposed for weather forecast text generation because similar weather conditions occur over time and should have similar forecast texts. CBR-METEO, a system for generating weather forecast texts was developed using a generic framework (jCOLIBRI) which provides modules for the standard components of the CBR architecture. The advantage in a CBR approach is that systems can be built in minimal time with far less human effort after initial consultation with experts. The approach depends heavily on the goodness of the retrieval and revision components of the CBR process. We evaluated CBR-METEO with NIST, an automated metric which has been shown to correlate well with human judgements for this domain. The system shows comparable performance with other NLG systems that perform the same task.

  1. Kittredge, R. , Polgue´re, A. and Goldberg, E. 1986. Synthesizing weather reports from formatted data. In Proceedings of the 11th. International Conference on Computational Linguistics, 563–565.
  2. Bourbeau, L. , Carcagno, D. , Goldberg, E. , Kittredge, R. , and Polgure, A. 1990. Bilingual generation of weather forecasts in an operations environment. In Proceedings of COLING'90, 318–320.
  3. Sigurd, B. , Willners, C. , Eeg-Olofsson, M. , and Johansson, C. 1992. Deep comprehension, generation and translation of weather forecasts (weathra). In COLING-92, 749–755.
  4. Coch, J. 1998. Interactive generation and knowledge administration in multimeteo. In Proceedings of the 9th International Workshop on NLG, 300–303.
  5. Rubinoff, R. 2000. Integrating text planning and linguistic choice without abandoning modularity: the IGEN generator. Computational Linguistics, 26(2):107–138.
  6. Sripada, S. , Reiter, E. and Davy, I. 2003. SumTime-Mousam: Configurable marine weather forecast generator. Expert Update, 6(3):4–1.
  7. Belz, A. 2007. Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models. Natural Language Engineering, 14:431–455.
  8. Reiter, E. , Sripada, S. and Robertson, R. 2003. Acquiring correct knowledge for natural language generation. Journal of Artificial Intelligence Research, 18:491–516.
  9. Dimitromanolaki, A. and Androutsopoulos, I. 2003. Learning to order facts for discourse planning in natural language generation. In Proc. of EACL Workshop on NLG.
  10. Glahn, H. 1970. Computer-produced worded forecasts. American Meteorological Society Bulletin, 51(12):1126–1131.
  11. Ruth, D. P. and Peroutka, M. R. 1993. The interactive computer worded forecast. In 9th International Conference on Interactive Information and Processing Systems for Meteorology, Oceanography and Hydrology, 321–326. American Meteorological Society.
  12. Goldberg, E. , Driedger, N. and Kittredge, R. 1994. Using natural-language processing to produce weather reports. IEEE Expert, 9:45–53.
  13. Reiter, E. and Robert Dale. 1995. Building applied natural language generation systems. Natural Language Engineering, 1:1–32.
  14. Belz, A. and Kow, E. 2009. System building cost vs. output quality in data-to-text generation. In Proceedings of 12th European Workshop on NLG.
  15. Belz, A. and Reiter, E. 2006. Comparing automatic and human evaluation of NLG systems. In Proceedings of EACL'06, 313–320.
  16. Gerva´s, P. 2001. Automatic Generation of Poetry using a CBR Approach. In Proceedings of the Conference of the Spanish Association for Artificial Intelligence (CAEPIA).
  17. Gerva´s, P. , D´?az-Agudo, B. , Peinado, F. and Herva´s, R. 2004. Story plot generation based on CBR. In 12th Conference on Applications and Innovations in Intelligent Systems.
  18. Aamodt, A. and Plaza, E. 1994. Case-based reasoning: Foundational issues, methodological variations and system approaches. Artificial Intelligence Communications (AICom), 7(1):39–59.
  19. D?az-Agudo, B. , Gonzalez-Calero, P. A. , Recio-Garc?a, J. A. and Sanchez, A. 2007. Building CBR systems with jCOLIBRI. Special Issue on Experimental Software and Toolkits of the Journal Science of Computer Programming, 69(1-3):68–75.
  20. Doddington, G. 2002. Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In Proceedings of ARPA Work- shop on Human Language Technology.
  21. Papineni, K. , Roukos, S. , Ward, T. and Zhu, W-J. 2002. BLEU: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 311–318.
  22. Lin, C-Y. and Hovy, E. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the Human Technology Conference (HLT-NAACL 03), 71–78.
  23. Belz, A. 2009. Prodigy-METEO: Pre-Alpha Release Notes. University of Brighton, UK, first edition.
  24. Craw, S. , Wiratunga, N. and Rowe, R. C. 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170:1175–1192.
  25. Sripada, S. , Reiter, E. , Hunter, J. and Yu, J. 2002. SUMTIME-METEO: Parallel corpus of naturally occurring forecast texts and weather data. Technical report, Department of Computer Science, University of Aberdeen
Index Terms

Computer Science
Information Sciences


Weather Forecast Text Reuse Text Generation Cbr Nlg