In this paper we present QuASIt, a Question Answering System for the Italian language, and the underlying cognitive architecture. The term cognitive is meant in the procedural semantics perspective, which states that the interpretation and/or production of a sentence requires the execution of some cognitive processes over both a perceptually grounded model of the world, and a linguistic knowledge acquired previously. We attempted to model these cognitive processes with the aim to make an artificial agent able both to understand and produce natural language sentences. The agent runs these processes on its inner domain representation using the linguistic knowledge also. In this sense, QuASIt is both a rule-based and ontology-based question answering system. In the model, rules are aimed at understanding the query in terms of the linguistic typology of the question, and enabling its semantic processing as regards the search for the answer in the structured knowledge from DBPedia Italian project. Also the free explicative text in support of the query is analyzed if available. QuASIt attempts to answer for both multiple choice and essay questions. The model is presented, the implementation of the system is detailed, and some experiments are discussed.
Pipitone, A., Tirone, G., & Pirrone, R. (2016). QuASIt: A cognitive inspired approach to question answering for the Italian language. In AI*IA 2016: Advances in Artificial Intelligence (pp.464-476). Berlin Heidelberg : Springer Verlag.
|Autori:||Pipitone, A.; Tirone, G.; Pirrone, R.|
|Titolo:||QuASIt: A cognitive inspired approach to question answering for the Italian language|
|Settore Scientifico Disciplinare:||Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni|
|Data di creazione:||2016-12-01|
|Nome del convegno:||15th International Conference on Italian Association for Artificial Intelligence, AIIA 2016|
|Luogo del convegno:||ita|
|Anno del convegno:||2016|
|Data di concessione:||2015|
|Data di pubblicazione:||2016|
|Numero di pagine:||13|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-319-49130-1_34|
|Citazione:||Pipitone, A., Tirone, G., & Pirrone, R. (2016). QuASIt: A cognitive inspired approach to question answering for the Italian language. In AI*IA 2016: Advances in Artificial Intelligence (pp.464-476). Berlin Heidelberg : Springer Verlag.|
|Tipologia:||0 - Proceedings (TIPOLOGIA NON ATTIVA)|
|Appare nelle tipologie:||0 - Proceedings (TIPOLOGIA NON ATTIVA)|