Abstract
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.
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Nicosia, M., Barlacchi, G., Moschitti, A. (2015). Learning to Rank Aggregated Answers for Crossword Puzzles. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_61
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DOI: https://doi.org/10.1007/978-3-319-16354-3_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
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