Modeling in constraint programming is a hard task that requires considerable expertise. Automated model reformulation aims at assisting a naive user in modeling constraint problems. In this context, formal specification languages have been devised to express constraint problems in a manner similar to natural yet rigorous specifications that use a mixture of natural language and discrete mathematics. Yet, a gap remains between such languages and the natural language in which humans informally describe problems. This work aims to alleviate this issue by proposing a method for detecting constraints in natural language problem descriptions using a structured-output classifier. To evaluate the method, we develop an original annotated corpus which gathers 110 problem descriptions from several resources. Our results show significant accuracy with respect to metrics used in cognate tasks.

Constraint detection in natural language problem descriptions / Kiziltan, Z.; Lippi, M.; Torroni, P.. - In: IJCAI. - ISSN 1045-0823. - 2016-:(2016), pp. 744-750. (Intervento presentato al convegno 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 tenutosi a usa nel 2016).

Constraint detection in natural language problem descriptions

Lippi M.;
2016

Abstract

Modeling in constraint programming is a hard task that requires considerable expertise. Automated model reformulation aims at assisting a naive user in modeling constraint problems. In this context, formal specification languages have been devised to express constraint problems in a manner similar to natural yet rigorous specifications that use a mixture of natural language and discrete mathematics. Yet, a gap remains between such languages and the natural language in which humans informally describe problems. This work aims to alleviate this issue by proposing a method for detecting constraints in natural language problem descriptions using a structured-output classifier. To evaluate the method, we develop an original annotated corpus which gathers 110 problem descriptions from several resources. Our results show significant accuracy with respect to metrics used in cognate tasks.
2016
25th International Joint Conference on Artificial Intelligence, IJCAI 2016
usa
2016
2016-
744
750
Kiziltan, Z.; Lippi, M.; Torroni, P.
Constraint detection in natural language problem descriptions / Kiziltan, Z.; Lippi, M.; Torroni, P.. - In: IJCAI. - ISSN 1045-0823. - 2016-:(2016), pp. 744-750. (Intervento presentato al convegno 25th International Joint Conference on Artificial Intelligence, IJCAI 2016 tenutosi a usa nel 2016).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1215135
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