Crossword Puzzle resolution in Italian using distributional models for clue similarity

Massimo Nicosia, Alessandro Moschitti

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
JournalUnknown Journal
Volume1653
Publication statusPublished - 2016

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Keywords

  • Distributional models
  • Information retrieval
  • Learning to rank

ASJC Scopus subject areas

  • Computer Science(all)

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