Titlebook: Language Processing and Knowledge in the Web; 25th International C Iryna Gurevych,Chris Biemann,Torsten Zesch Conference proceedings 2013 S

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A Joint Inference Architecture for Global Coreference Clustering with Anaphoricity,s are discriminatively clustered with discourse entities established by an anaphoricity classifier. Our entity-based coreference architecture is realized in a joint inference setting to compensate for erroneous anaphoricity classifications and avoids local coreference misclassifications through glob

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Linguistic and Statistically Derived Features for Cause of Death Prediction from Verbal Autopsy Tex on work in progress to develop methods for predicting Cause of Death from Verbal Autopsy (VA) documents recommended for use in low-income countries by the World Health Organisation. VA documents contain both coded data and open narrative. The task is formulated as a Text Classification problem and

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Probabilistic Explicit Topic Modeling Using Wikipedia,th relevant knowledge sources such as Wikipedia, and they can be difficult to interpret due to the lack of meaningful topic labels. Furthermore, the topic analysis suffers from a lack of identifiability between topics across independently analyzed corpora but also across distinct runs of the algorit

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,Extending the TüBa-D/Z Treebank with GermaNet Sense Annotation,uation. The underlying textual resource, the TüBa-D/Z treebank, is a German newspaper corpus already manually enriched with high-quality, manual annotations at various levels of grammar. The sense inventory used for tagging word senses is taken from GermaNet [8,9], the German counterpart of the Prin

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Topic Modeling for Word Sense Induction,istributions as a means to estimate sense distributions. We provide these distributions as input to a clustering algorithm in order to automatically distinguish between the senses of semantically ambiguous words. The results of our evaluation experiments indicate that the performance of our approach

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Named Entity Recognition in Manipuri: A Hybrid Approach,atistical approach (Conditional Random Field, CRF) and rule-based approach. The rule-based approach helps in defining various unique word features that are used in accurately classifying the Named Entities by the CRF classifier. With small corpus size, this hybrid approach proves to have Recall, Pre

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