Abstract
Coreference resolution is the task of identifying which noun phrases or mentions refer to the same real-world entity in a text or a dialogue. This is an essential task in many of the NLP applications such as information extraction, question answering system, summarization, machine translation and in information retrieval systems. Coreference Resolution is traditionally considered as pairwise classification problem and different classification techniques are used to make a local classification decision. We are using Tree-CRF for this task. With Tree-CRF we make a joint prediction of the anaphor and the antecedent. Tree-based Reparameterization (TRP) for approximate inference is used for the parameter learning. TRP performs an exact computation over the spanning trees of a full graph. This helps in learning the long distance dependency. The approximate inference methodology does a better convergence. We have used the parsed tree from the OntoNotes, released for CoNLL shared task 2011. We derive features from the parse tree. We have used the different genre data for the experiments. The results are encouraging.
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Ram, R.V.S., Devi, S.L. (2012). Coreference Resolution Using Tree CRFs. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28604-9_24
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DOI: https://doi.org/10.1007/978-3-642-28604-9_24
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