Abstract
We present our work on automatic identification of cause-effect relations in a given Tamil text. Based on the analysis of causal constructions in Tamil, we identified a set of causal markers for Tamil and arrived at certain features used to develop our language model. We manually annotated a Tamil corpus of 8648 sentences for cause-effect relations. With this corpus, we developed the model for identifying causal relations using the machine learning technique, Conditional Random Fields (CRFs). We performed experiments and the results are encouraging. We performed an error analysis of the results and found that the errors can be attributed to some very interesting structural interdependencies between closely occurring causal relations. After comparing these structures in Tamil and English, we claim that at discourse level, the complexity of structural interdependencies between causal relations is more complex in Tamil than in English due to the free word order nature of Tamil.
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S., M., Rao, P.R.K., Lalitha Devi, S. (2011). Automatic Identification of Cause-Effect Relations in Tamil Using CRFs. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_25
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DOI: https://doi.org/10.1007/978-3-642-19400-9_25
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