Abstract
Classifying the sentences that describe Events is an important task for many applications. In this chapter, Event patterns are identified and extracted at sentence level using term features. The terms that trigger Events along with the sentences are extracted from Web documents. The sentence structures are analysed using POS tags. A hierarchal sentence classification model is presented by considering specific term features of the sentence, and the rules are derived. The rules fail to define a clear boundary between the patterns and create ambiguity and impreciseness. To overcome this, suitable fuzzy rules are derived which give importance to all term features of the sentence. The fuzzy rules are constructed with more variables and generate sixteen patterns. Artificial neuro-fuzzy inference system (ANFIS) model is presented for training and classifying the sentence patterns for capturing the knowledge present in sentences. The obtained patterns are assigned linguistic grades based on previous classification knowledge. These grades represent the type and quality of information in the patterns. The membership function is used to evaluate the fuzzy rules. The patterns share the membership values between [0–1] which determines the weights for each pattern. Later, higher weighted patterns are considered to build Event Corpus, which helps in retrieving useful and interested information of Event Instances. The performance of the presented approach classification is evaluated for ‘Crime’ Event by crawling documents from WWW and also evaluated for benchmark dataset for ‘Die’ Event. It is found that the performance of the presented approach is encouraging when compared with recently proposed similar approaches.
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
Abuleil, S. (2007). Using NLP techniques for tagging events in Arabic text. In Proceedings of 19th IEEE International Conference on Tools with AI (pp. 440–443). New York: IEEE Press.
ACE (Automatic Content Extraction) English Annotation Guidelines for events Version 5.4.3 2005.07.01 Linguistic Data Consortium. http://www.ldc.upenn.edu.
Allan, J., Jaime, C., George, D., Jonathon, Y., & Yiming, Y. (1998). Topic detection and tracking pilot study.
Allan, J., Wade, C., & Bolivar, A. (2003). Retrieval and novelty detection at the sentence level. In: Proceedings of SIGIR (pp. 314–321).
Aone, C., & Ramos-Santacruz, M. (2000). REES: A large-scale relation and event extraction system. In: Proceedings of 6th Conference on Applied Natural Language Processing (pp. 76–83). Washington: Morgan Kaufmann Publishers Inc.
Breiman, L., Friedman, J., Olshen, R. A., & Charles, J. S. (1985). Classification and regression trees. Monterey, CA: Wadsworth and Brooks.
Creswell, C., Beal, J. M., Chen, J., Cornell, L. T., Nilsson, L., Srihari, R. K. (2006). Automatically extracting nominal mentions of events with a bootstrapped probabilistic classifier. In Proceedings of COLING/ACL 2006 Main Conference Poster Sessions (pp. 168–175).
Cohen, K. B., Verspoor, K., Johnson, H., Roeder, C., Ogren, P., Baumgartner, W., et al. (2009). High-precision biological event extraction with a concept recognizer. In Proceedings of BioNLP09 Shared Task Workshop (pp. 50–58).
David, A. (2006) Stages of event extraction. In Proceedings of COLING/ACL 2006 Workshop on Annotating and Reasoning about Time and Events (pp. 1–8).
Filatova, E., & Hatzivassiloglou, V. (2004). Event-based extractive summarization. In Proceedings of ACL 2004 Workshop on Summarization, Barcelona, Spain (pp. 104–111).
Grishman, R., & Sundheim, B. (1996). Message understanding conference-6: A brief history. In Proceedings of Computational Linguistics.
He, X., Chu, W. C., Yang, H., & Yang, S. J. H. (1999). A new approach to verify rule-based systems using petri nets. In Proceedings of International 23rd IEEE Conference on Computer Software and Applications Conference (COMPSAC’99) (pp. 462–467).
Hristidis, V., Valdivia, O., Vlachos, M., & Yu, P. S. (2006). Continuous term search on multiple text streams. In Proceedings of CIKM, Arlington, VA (pp. 802–803).
Hung, S. H., Lin, C. H., & Hong, J. S. (2010). Web mining for event-based commonsense knowledge using lexico-syntactic pattern matching and semantic role labeling. Journal of Expert Systems with Applications, 37, 341–347.
Jang, J. S. R. (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics, 23(No. 5, Issue 6), 665–685.
Makoto, M., Rune, S., Jjin-Dong, K., & Junichi, T. (2010). Event extraction with complex event classification using rich features. Journal of Bioinformatics and Computational Biology, 8(1), 131–146.
McCracken, N., Ozgencil, N. E., & Symonenko, S. (2006). Combining techniques for event extraction in summary reports. In Proceedings of AAAI 2006 Workshop Event Extraction and Synthesis (pp. 7–11).
Naughton, M., Stokes, N., & Carthy, J. (2010). Sentence-level event classification in unstructured texts. Information Retrieval, 13, 132–156.
Xu, F., Uszkoreit, H., & Li, H. (2006). Automatic event and relation detection with seeds of varying complexity. In Proceedings of AAAI 2006 Workshop Event Extraction and Synthesis, Boston (pp. 491–498).
Yang, Y., Pierce, T., & Carbonell, J. (1998). A study on retrospective and on-line event detection. In Proceedings of SIGIR (pp. 28–36).
Yang, Y., Zhang, J., Carbonell, J., & Jin, C. (2002). Topic-conditioned novelty detection. In: Proceedings of SIGKDD (pp. 688–693).
Zhao, Q., Bhowmick, S. S., & Sun, A. (2006). iWed: An integrated multigraph cut-based approach for detecting Events from a website. In Proceedings of 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining (pp. 351–360). Berlin, Heidelberg: Springer.
Zhao, Q., Liu, T. Y., Bhowmick, S. S., Ma, W.-Y. (2006). Event detection from evolution of click-through data. In Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 484–493). New York, NY, USA: ACM.
Zhou, W., Liu, Z., & Kong, Q. (2008). A survey of Event-based knowledge processing. Chinese Journal of Computer Science, 33(2), 160–162. (in Chinese).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Shaila, S.G., Vadivel, A. (2018). Information Classification and Organization Using Neuro-Fuzzy Model for Event Pattern Retrieval. In: Textual and Visual Information Retrieval using Query Refinement and Pattern Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-13-2559-5_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-2559-5_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2558-8
Online ISBN: 978-981-13-2559-5
eBook Packages: Computer ScienceComputer Science (R0)