Abstract
The contents generated by the users on the Web play a vital role for researchers to extract knowledge from these contents. Users write their views by making comparison between two or more than two features in a product domain. Extracting these reviews from the Web helps in improving the business from competitors. In this paper, a method to extract the comparative sentences from the text documents using a rule-based shallow parser is proposed. A shallow parser holds a nonoverlapping area of text and allows extracting the part of the text based on the given rule or grammar. In order to identify and classify comparatives from text documents various rules were generated. The proposed technique is divided into two tasks: first, obtain the rules to identify the comparative sentences from various text documents, and second, classify the text documents into different categories of comparatives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, B.: Searching opinions in user-generated contents. In: Invited Talk At the Sixth Annual Emerging Information Technology Conference (EITC-06), Aug 10–12, Dallas, Texas (2006)
Kurashima, T., Bessho, K., Toda, H. et al.: Ranking Entities Using Comparative Relations. DEXA 2008, LNCS 5181, pp. 124–133, 2008 © Springer, Berlin, Heidelberg (2008)
Harb, A., Dray, M.: Web opinion mining: how to extract opinions from blogs. In: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology. ACM New York, NY, USA (2008)
Ganapathibhotla, M., Liu, B.: Mining opinions in comparative sentences. In: The Proceedings of the 22nd International Conference on Computational Linguistics 2008. Stroudsburg, PA, USA
Abney, S.: Principle—based parsing: Computing and Psycholinguistics, First edn, pp. 408. Springer, Dordrecht, ISBN: 0792311736 (1991)
Friedman, C.: A General Computational Treatment of the Comparative, Association of Computational Linguistics, pp. 161–168. Stroudsburg, PA (1989)
Staab, S., Hahn, U.: Comparatives in Context. In: National Conference on AI. National Conference on Artificial Intelligence, pp. 616–621 (1997)
Bresnan, J.W.: Syntax of the comparative clause construction in English. Linguist. Inquiry 4(3), 275–343 (1973)
Jindal, N., Liu, B.: Identifying comparative sentences in text documents. In: Proceedings of SIGIR’06, pp. 244–251 (2006)
Jindal, N., Liu, B.: Mining comparative sentences and relations. In: The Proceedings of the 21st Conference on Artificial Intelligence AAAI-06, AAAI Press
Sarawagi, S.: CRF Project page. http://crf.sourceforge.net/ (2004)
Yang, S., Ko, Y.: extracting comparative sentences from korean text documents using comparative lexical patterns and machine learning techniques. In: Proceedings of ACL-IJNLP: Short Papers, 153–156 (2009)
Yang, S., Ko, Y.: Finding relevant features for Korean comparative sentence extraction. Pattern Recogn. Lett. 32(2), 293–296 (2011)
Huang, X., Wan, X., Yang, J., Xiao, J.: Learning to Identify Comparative Sentences in Chinese Text. PRICAI 2008, LNAI 5351, pp. 187–198 © Springer, Berlin, Heidelberg (2008)
Li, S., Lin, C.Y., Song, Y.I., Li, Z.: Comparable entity mining from comparative questions. In: Proceedings of ACL’10, 650–658 (2010)
Park, D.H., Blake, C.: Identifying comparative claim sentences in full-text scientific articles. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pp. 1–9, Jeju, Republic of Korea, 12 July 2012. © 2012 Association for Computational Linguistics
Ganapathibhotla, M., Liu, B.: Mining opinions in comparative sentences. In: International Conference on Computational Linguistics (Coling). Manchester, UK (2008)
Gu, Y.H., Yoo, S.J.: Rules for mining comparative online opinions. In: 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology (2009)
Li, X., Roth, D.: Exploring evidence for shallow parsing. In: Proceedings of the Workshop Computer National Language Learning. doi: 10.3115/1117822.1117826
Pierce, D.R.: Cost-Effective Machine Learning Strategies for Shallow Parsing, 1st edn, p. 183. Cornell University, USA (2003)
Kim, S., Hovy, E.: Automatic detection of opinion bearing words and sentences. In Proceedings of ACL’06 (2006)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Saritha, S.K., Pateriya, R.K. (2016). Rule-Based Shallow Parsing to Identify Comparative Sentences from Text Documents. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications . Springer, Singapore. https://doi.org/10.1007/978-981-10-0287-8_33
Download citation
DOI: https://doi.org/10.1007/978-981-10-0287-8_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0286-1
Online ISBN: 978-981-10-0287-8
eBook Packages: EngineeringEngineering (R0)