Natural language semantic model for arithmetic sentences
Pages 175 - 179
Abstract
Over the past 40 years, many studies related to natural language processing have been conducted in various fields, such as text summarization, human-computer interaction, language translation and natural language interfaces to a database. Most of these studies only cover general usage levels of natural languages ignoring processing-type sentences such as arithmetic sentences, sorting, and grouping. This research presents a Natural Language Semantic Model for Arithmetic Sentences (NLSMAS) which can define a meaning and calculate the result for arithmetic sentences, one of the most widely used processing-type sentences. Data for this research were10,000 arithmetic sentences, which were divided into four categories based on calculation difficulty. These sentences were analyzed focusing on sentence structures and semantic rules to create a Semantic GrammarGraph (SGG), the main part of NLSMAS. Finally, 12,000 arithmetic sentences from 300 samples were used as input for the evaluation of NLSMAS's performance. The result showed that NLSMAS can definearithmetic meanings and produce results with very high accuracy at 98.68 percent. The incorrect results were caused by typing error, grammatical error, and ambiguous sentences.
References
[1]
Foong, O. M., Yong, S. P., and Jaid, F. A. 2015. Text Summarization Using Latent Semantic Analysis Model in Mobile Android Platform. Paper presented at the 2015 9th Asia Modelling Symposium (AMS) (Sept. 2015), 35--39.
[2]
Harris, I. G. 2012. Extracting design information from natural language specifications. Paper presented at the Design Automation Conference (DAC), 2012 49th ACM/EDAC/IEEE(June 2012), 1252--1253.
[3]
Kazar, O., Hamza, S., Hind, B., and Bourekkache, L. S. 2017. Semantic natural language translation based on ontologies combination. Paper presented at The 8th International Conference on Information Technology ICIT (The Amman, Jordan, May 17--18, 2017).
[4]
Hyeon, H. J., Taylor, J., and Matson, E. T. 2014. Natural Multi-Language Interaction between Firefighters and Fire Fighting Robots. In Web Intelligence and Intelligent Agent Technologies, IEEE/WIC/ACM International Joint Conferences (Aug. 11--14, 2014), 183--89.
[5]
Shah, A., Pareek, J., Patel, H., and Panchal, N. 2013. NLKBIDB - Natural language and keyword based interface to database. Paper presented at the Advances in Computing, Communications, and Informatics ICACCI (TheMysore, India, Aug. 22--25, 2013), 1569--1576.
[6]
Priyadarshini, R., Tamilselvan, L., Khuthbudin, T., Saravanan, S., and Satish, S. 2015. Semantic Retrieval of Relevant Sources for Large Scale Virtual Documents. Procedia Computer Science, 54, 371--379.
[7]
Androutsopoulos, I., Ritchie, G.D., and ThanischP. 1995, Natural Language Interfaces to Databases-An Introduction In NaturalLanguage Engineering, Vol I, part I, 29--81.
[8]
Hussein, A. S. 2016. Visualizing document similarity using n-grams and latent semantic analysis. Paper presented at the 2016 SAI Computing Conference (July 13--15 2016), 269--79
[9]
Peiyou, S., Anhei, S., Phipps, D., Tiwari, M., Wallach, D. S., Crandall, J. R., and Luger, G. F. 2012. Language without words: A pointillist model for natural language processing. Paper presented at the Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (Nov. 20--24,2012),11--15
[10]
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., and Chanona-Hernández, L. 2014. Syntactic N-grams as machine learning features for natural language processing. Expert Systems with Applications, 41(3), 853--860.
[11]
Ceccato, S. Correlational Analysis and Mechanical Translation. In Machine Translation, A. D. Booth, Ed. Amsterdam(The Netherlands, North Holland, 1967), 77--135.
[12]
Song, X., Wang, X., and Hu, X. 2016. Semantic pattern mining for text mining. Paper presented at the 2016 IEEE International Conference on Big Data (Dec. 5--8. 2016), 150--155.
[13]
Xuan, X., Liu, J., and Yang, J. 2012. Research on the Natural Language Querying for Remote Sensing Databases. Paper presented at the Computer Science & Service System(Aug. 11--13, 2012), 228--231.
[14]
Mezghanni, I. B., and Gargouri, F. 2017. Deriving ontological semantic relations between Arabic compound nouns concepts. Journal of King Saud University - Computer and Information Sciences, 29(2), 212--228.
[15]
Pal, K. 2017. A Semantic Web Service Architecture for Supply Chain Management. Procedia Computer Science, 109, 999--1004.
[16]
Suthiwong P. 1999.Thai Language (14th Edition), Thai WattanaPanich Printing Company Limited, Bangkok, Thailand.
[17]
Shoichi I. and Preeya I. 2005.A Reference Grammar of Thai, Cambridge University Press, United Kingdom, 73--80.
[18]
Kamchai T. 1996.Thai Language, Amorn Printing, Bangkok, Thailand,407--414.
[19]
Uppakit S. 1990.Thai Language Principal, Thai Wattanapanich Publishing, Bangkok, Thailand,78--82.
[20]
The Royal Institute. 2013.Dictionary of The Royal Institute., Bangkok, Thailand.
[21]
Jirawan P. andAmporn T. 2013.Foreign Language in Thai, O S Printing House, Bangkok, Thailand.
[22]
Tapsai, C., Meesad, P., and Haruechaiyasak, C. 2016. TLS-ART: Thai Language Segmentation by Automatic Ranking Trie. Paper presented at The 9th International Conference Autonomous Systems (October 2016).
Index Terms
- Natural language semantic model for arithmetic sentences
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Published In
November 2017
545 pages
ISBN:9781450353656
DOI:10.1145/3162957
- Conference Chairs:
- Jalel Ben-Othman,
- Feng Gang,
- Program Chairs:
- Jain-Shing Liu,
- Masayuki Arai
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Published: 24 November 2017
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ICCIP 2017
ICCIP 2017: 2017 the 3rd International Conference on Communication and Information Processing
November 24 - 26, 2017
Tokyo, Japan
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