Generating minimal definite descriptions
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Tracking and summarizing news on a daily basis with Columbia's Newsblaster
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Evaluating algorithms for the generation of referring expressions using a balanced corpus
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
Generating referring expressions in context: the GREC task evaluation challenges
Empirical methods in natural language generation
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Generating referring expressions has received considerable attention in Natural Language Generation. In recent years we start seeing deployments of referring expression generators moving away from limited domains with custom-made ontologies. In this work, we explore the feasibility of using large scale noisy ontologies (folksonomies) for open domain referring expression generation, an important task for summarization by re-generation. Our experiments on a fully annotated anaphora resolution training set and a larger, volunteer-submitted news corpus show that existing algorithms are efficient enough to deal with large scale ontologies but need to be extended to deal with undefined values and some measure for information salience.