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.
CITATION STYLE
Nicosia, M., Barlacchi, G., & Moschitti, A. (2015). Learning to rank aggregated answers for crossword puzzles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9022, pp. 556–561). Springer Verlag. https://doi.org/10.1007/978-3-319-16354-3_61
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