Paper: Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles

ACL ID W14-1605
Title Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles
Venue International Conference on Computational Natural Language Learning
Session
Year 2014
Authors

In this paper, we study the impact of rela- tional and syntactic representations for an interesting and challenging task: the au- tomatic resolution of crossword puzzles. Automatic solvers are typically based on two answer retrieval modules: (i) a web search engine, e.g., Google, Bing, etc. and (ii) a database (DB) system for access- ing previously resolved crossword puz- zles. We show that learning to rank models based on relational syntactic structures de- fined between the clues and the answer can improve both modules above. In particu- lar, our approach accesses the DB using a search engine and reranks its output by modeling paraphrasing. This improves on the MRR of previous system up to 53% in ranking answer candidates and greatly im- pacts on the resolution accuracy of cross- word pu...