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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Learning to Rank Aggregated Answers for Crossword Puzzles
verfasst von : Massimo Nicosia, Gianni Barlacchi, Alessandro Moschitti
Erschienen in: Advances in Information Retrieval
Verlag: Springer International Publishing
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In this paper, we study methods for improving the quality of automatic extraction of answer candidates for automatic resolution of crossword puzzles (CPs), which we set as a new IR task. Since automatic systems use databases containing previously solved CPs, we define a new effective approach consisting in querying the database (DB) with a search engine for clues that are similar to the target one. We rerank the obtained clue list using state-of-the-art methods and go beyond them by defining new learning to rank approaches for aggregating similar clues associated with the same answer.