Crossword Puzzle resolution in Italian using distributional models for clue similarity
Massimo Nicosia, Alessandro Moschitti · Institutional Research Information System (Università degli Studi di Trento) · 2016
Leveraging previous knowledge is essential for the automatic resolution of Crossword Puzzles (CPs).Clues from a new crossword may have appeared in the past, verbatim or paraphrased, and thus we can extract similar clues using information retrieval (IR) techniques.The output of a search engine implementing the retrieval model can be refined using learning to rank techniques: the goal is to move the clues that have the same answer of the query clue to the top of the result list.The accuracy of a crossword solver heavily depends on the quality of the latter.In previous work, the lists generated by an IR engine were reranked with a linear model by exploiting the multiple occurrences of an answer in such lists.In this paper, following our recent work on CP resolution for the English language, we create a labelled dataset for Italian, and propose (i) a set of reranking baselines and (ii) a neural reranking model based on distributed representations of clues and answers.Our neural model improves over our proposed baselines and the state of the art.