Abstract
Temporal annotation of plain text is considered as a useful component of modern information retrieval tasks. In this work, two approaches for identification and classification of temporal entities in Hindi are developed and analyzed. Firstly, a rule based approach is developed, which takes plain text as input and based on a set of hand-crafted rules, produces a tagged output with identified temporal expressions. This approach is shown to have a strict F1-measure of 0.83. In the other approach, a CRF based classifier is trained with human tagged data and is then tested on a test dataset. The trained classifier identifies the temporal expressions from plain text and further classifies them to various classes. This approach is shown to have a strict F1-measure of 0.78. In this process a reusable gold standard dataset for temporal tagging in Hindi was developed. Named the ILTIMEX2012 corpus, it consists of 300 manually tagged Hindi news documents.
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References
Pustejovsky, J.: TERQAS: Time and Event recognition for question answering systems. In: ARDA Workshop (2002)
Pustejovsky, J., Castano, J., Ingria, R., Sauri, R., Gaizauskas, R., Setzer, A., Katz, G., Radev, D.: TimeML: Robust specification of event and temporal expressions in text. In: New Directions in Question Answering 2003, pp. 28–34 (2003)
Mani, I., Schiffman, B.: Temporally anchoring and ordering events in news. In: Time and Event Recognition in Natural Language. John Benjamins (2005)
Swan, R., Allan, J.: Automatic generation of overview timelines. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49–56. ACM (2000)
Allan, J., Gupta, R., Khandelwal, V.: Temporal summaries of new topics. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 10–18. ACM (2001)
Majumder, P.: Forum for Information Retrieval Evaluation 2011 (2011), http://www.isical.ac.in/~fire/2011/slides/fire.2011.majumder.prasenjit.pdf
Mani, I., Wilson, G., Sundheim, B., Ferro, L.: A multilingual approach to annotating and extracting temporal information. In: Proceedings of the Workshop on Temporal and Spatial Information Processing, vol. 1, p. 12. Association for Computational Linguistics (2001)
Negri, M., Marseglia, L.: Recognition and Normalization of Time Expressions: ITC-irst at TERN 2004. Rapport Interne, ITC-irst, Trento (2004)
Saquete, E., Muñoz, R., MartÃnez-Barco, P.: Event ordering using TERSEO system. Data & Knowledge Engineering 58(1), 70–89 (2006)
Strötgen, J., Gertz, M.: HeidelTime: High quality rule-based extraction and normalization of temporal expressions. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 321–324 (2010)
Hacioglu, K., Chen, Y., Douglas, B.: Automatic time expression labeling for english and chinese text. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 548–559. Springer, Heidelberg (2005)
MUC-7: Message Understanding Conference 1998. In: Proceedings of the Seventh Message Understanding Conference, DARPA (1998)
Shokouhi, M.: SIGIR 2012 Workshop on Time Aware Information Access (2012), http://research.microsoft.com/en-us/people/milads/taia2012.aspx
Mazur, P.: TIMEX Portal (2008), http://www.timexportal.info/
MITRE-Corporation: TIDES Temporal Annotation Guide. The MITRE Corporation (June 2001)
MITRE-Corporation: 2005 Standard for the Annotation of Temporal Expressions. The MITRE Corporation (April 2005)
Mazur, P., Dale, R.: An intermediate representation for the interpretation of temporal expressions. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, pp. 33–36. Association for Computational Linguistics (2006)
Ahn, D., Rantwijk, J., Rijke, M.: A Cascaded Machine Learning Approach to Interpreting Temporal Expressions. In: HLT-NAACL, pp. 420–427 (2007)
NIST: The ACE 2004 Evaluation Plan, http://www.itl.nist.gov/iad/mig/tests/ace/2004/doc/ace04-evalplan-v7.pdf (2004)
NIST: Automatic Content Extraction (2004), http://www.itl.nist.gov/iad/mig/tests/ace/2004/index.html
Cunningham, H.: GATE, a general architecture for text engineering. Computers and the Humanities 36(2), 223–254 (2002)
Bharati, A., Chaitanya, V., Sangal, R., Ramakrishnamacharyulu, K.: Natural language processing: a Paninian perspective. Prentice-Hall of India, New Delhi (1995)
Jha, G.N.: The TDIL Program and the Indian Langauge Corpora Intitiative(ILCI). In: LREC (2010)
Kodu, T.: CRF++: Yet another CRF toolkit (2005), http://crfpp.googlecode.com/svn/trunk/doc/index.html
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Ramrakhiyani, N., Majumder, P. (2013). Temporal Expression Recognition in Hindi. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_72
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DOI: https://doi.org/10.1007/978-3-319-03844-5_72
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