Learning to rank aggregated answers for crossword puzzles

Massimo Nicosia, Gianni Barlacchi, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages556-561
Number of pages6
Volume9022
ISBN (Print)9783319163536
Publication statusPublished - 2015
Event37th European Conference on Information Retrieval Research, ECIR 2015 - Vienna, Austria
Duration: 29 Mar 20152 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9022
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other37th European Conference on Information Retrieval Research, ECIR 2015
CountryAustria
CityVienna,
Period29/3/152/4/15

Fingerprint

Search engines
Search Engine
Target
Learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

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). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9022). Springer Verlag.

Learning to rank aggregated answers for crossword puzzles. / Nicosia, Massimo; Barlacchi, Gianni; Moschitti, Alessandro.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9022 Springer Verlag, 2015. p. 556-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9022).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9022, Springer Verlag, pp. 556-561, 37th European Conference on Information Retrieval Research, ECIR 2015, Vienna, Austria, 29/3/15.
Nicosia M, Barlacchi G, Moschitti A. 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. Springer Verlag. 2015. p. 556-561. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Nicosia, Massimo ; Barlacchi, Gianni ; Moschitti, Alessandro. / Learning to rank aggregated answers for crossword puzzles. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9022 Springer Verlag, 2015. pp. 556-561 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{8f0b673593a540a782e74fbf1d69a713,
title = "Learning to rank aggregated answers for crossword puzzles",
abstract = "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.",
author = "Massimo Nicosia and Gianni Barlacchi and Alessandro Moschitti",
year = "2015",
language = "English",
isbn = "9783319163536",
volume = "9022",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "556--561",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Learning to rank aggregated answers for crossword puzzles

AU - Nicosia, Massimo

AU - Barlacchi, Gianni

AU - Moschitti, Alessandro

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84925423271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84925423271&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9783319163536

VL - 9022

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 556

EP - 561

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -