Name
Abstract | ||
|---|---|---|
Crossword puzzles are popular word games that require not only a large vocabulary, but also a broad knowledge of topics. Answering each clue is a natural language task on its own as many clues contain nuances, puns, or counter-intuitive word definitions. Additionally, it can be extremely difficult to ascertain definitive answers without the constraints of the crossword grid itself. This task is challenging for both humans and computers. We describe here a new crossword solving system, Cruciform. We employ a group of natural language components, each of which returns a list of candidate words with scores when given a clue. These lists are used in conjunction with the fill intersections in the puzzle grid to formulate a constraint satisfaction problem, in a manner similar to the one used in the Dr. Fill system. We describe the results of several of our experiments with the system. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Computer science,Constraint satisfaction problem,Natural language,Artificial intelligence,Natural language processing,Vocabulary,Machine learning,Grid |
DocType | Volume | Citations |
Journal | abs/1611.02360 | 0 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dragomir Radev | 1 | 5167 | 374.13 |
| Rui Zhang | 2 | 1107 | 145.40 |
| Steven R. Wilson | 3 | 12 | 7.21 |
| Derek Van Assche | 4 | 0 | 0.34 |
| Henrique Spyra Gubert | 5 | 0 | 0.34 |
| Alisa Krivokapic | 6 | 0 | 0.34 |
| MeiXing Dong | 7 | 0 | 0.34 |
| Chongruo Wu | 8 | 28 | 3.39 |
| Spruce Bondera | 9 | 0 | 0.34 |
| Luke Brandl | 10 | 0 | 0.34 |
| Jeremy Dohmann | 11 | 0 | 0.34 |