Abstract
“To improve is to change; to be perfect is to change often.”—Winston Churchill
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
DARPA stands for Defense Advanced Research Projects Agency, which is an agency of the United States Department of Defense. It is responsible for the development of emerging technologies for use by the military, and often funds academic research efforts.
Bibliography
C. Aggarwal and K. Subbian. Event detection in social streams. SDM Conference, 2012.
C. Aggarwal and P. Yu. On clustering massive text and categorical data streams. Knowledge and Information Systems, 24(2), pp. 171–196, 2010.
J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang. Topic detection and tracking pilot study final report. CMU Technical Report, Paper 341, 1998.
H. Becker, M. Naaman, and L. Gravano. Beyond Trending Topics: Real-World Event Identification on Twitter. ICWSM Conference, pp. 438–441, 2011.
D. Beeferman, A. Berger, and J. Lafferty. Statistical models for text segmentation. Machine Learning, 34(1–3), pp. 177–210, 1999.
D. Blei and P. Moreno. Topic segmentation with an aspect hidden Markov model. ACM SIGIR Conference, pp. 343–348, 2001.
N. Chambers, S. Wang, and D. Jurafsky. Classifying temporal relations between events. Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pp. 173–176, 2007.
F. Choi. Advances in domain independent linear text segmentation. North American Chapter of the Association for Computational Linguistics Conference, pp. 26–33, 2000.
F. Choi, P. Wiemer-Hastings, and J. Moore. Latent semantic analysis for text segmentation. EMNLP, 2001.
J. Eisenstein and R. Barzilay. Bayesian unsupervised topic segmentation. Conference on Empirical Methods in Natural Language Processing, pp. 334–343, 2008.
E. Erosheva, S. Fienberg, and J. Lafferty. Mixed-membership models of scientific publications. Proceedings of the National Academy of Sciences, 101, pp. 5220–5227, 2004.
M. Hearst. TextTiling: Segmenting text into multi-paragraph subtopic passages. Computational Linguistics, 23(1), pp. 33–64, 1997.
R. Kannan, H. Woo, C. Aggarwal, and H. Park. Outlier detection for text data. SDM Conference, 2017.
J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML Conference, pp. 282–289, 2001.
X. Ling and D. Weld. Temporal information extraction. AAAI, pp. 1385–1390, 2010.
D. Litman and R. Passonneau. Combining multiple knowledge sources for discourse segmentation. Association for Computational Linguistics, pp. 108–115, 1995.
I. Mani and G. Wilson. Robust temporal processing of news. ACL Conference, pp. 69–76, 2000.
A. McCallum, D. Freitag, and F. Pereira. Maximum entropy Markov models for information extraction and segmentation. ICML Conference, pp. 591–598, 2000.
D. McClosky, M. Surdeanu, and C. Manning. Event extraction as dependency parsing. Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 1626–1635, 2011.
J. Ponte and W. Croft. Text segmentation by topic. International Conference on Theory and Practice of Digital Libraries, pp. 113–125, 1997.
J. Pustejovsky et al. The timebank corpus. Corpus Linguistics, pp. 40, 2003.
J. Pustejovsky et al. TimeML: Robust specification of event and temporal expressions in text. New Directions in Question Answering, 3. pp. 28–34, 2003.
A. Ritter, Mausam, O. Etzioni, and S. Clark. Open domain event extraction from twitter. ACM KDD Conference, pp. 1104–1102, 2012.
A. Ritter, S. Clark, Mausam, and O. Etzioni. Named entity recognition in tweets: an experimental study. Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534, 2011.
T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors. World Wide Web Conference, pp. 851–860, 2010.
G. Salton and J. Allan. Selective text utilization and text traversal. Proceedings of ACM Hypertext, 1993.
G. Salton, J. Allan, and C. Buckley. Approaches to passage retrieval in full text information systems. ACM SIGIR Conference, pp. 49–58, 1997.
G. Salton, A. Singhal, M. Mitra, and C. Buckley. Automatic text structuring and summarization. Information Processing and Management, 33(2), pp. 193–207, 1997.
R. Sauri, R. Knippen, M. Verhagen, and J. Pustejovsky. Evita: a robust event recognizer for QA systems. Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 700–707, 2005.
H. Sayyadi, M. Hurst, and A. Maykov. Event detection and tracking in social streams. ICWSM Conference, 2009.
J. Yamron, I. Carp, L. Gillick, S. Lowe, and P. van Mulbregt. A hidden Markov model approach to text segmentation and event tracking. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 333–336, 1998.
Y. Yang, T. Pierce, and J. Carbonell. A study of retrospective and online event detection. ACM SIGIR Conference, pp. 28–36, 1998.
J. Zhang, Z. Ghahramani, and Y. Yang. A probabilistic model for online document clustering with application to novelty detection. NIPS Conference, pp. 1617–1624, 2004.
http://www.nltk.org/api/nltk.tokenize.html#nltk.tokenize.texttiling.TextTilingTokenizer
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Aggarwal, C.C. (2018). Text Segmentation and Event Detection. In: Machine Learning for Text. Springer, Cham. https://doi.org/10.1007/978-3-319-73531-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-73531-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73530-6
Online ISBN: 978-3-319-73531-3
eBook Packages: Computer ScienceComputer Science (R0)