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28 août 2017
- doi: 10.4000/books.aaccademia.1970
- eisbn: 9788899982553
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
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linguistica computazionale riconoscimento telefonico articolare annotazione fattualità degli eventi entità chiamata rEcognition e collegamenti nei tweet italiani etichettare per messaggi social media classificazione polarità sentimenti linguistique computationelle reconnaissance téléphonique articulatoire annotation de facturation de l'événement entité appelée rEcognition et liens dans le tweets italien étiqueter les messages des médias sociaux classement polarité sentiments computational linguistics articulatory phone recognition event factuality annotation named entity rEcognition and linking in italian tweets tagging for italian social media texts sentiment polarity classification LAN009000 Linguistics CFSujets proches
PE ratio Earnings multiple Earnings-price ratio Stocks--Price-earnings ratios Pension plans Employee pension trusts Pension funds Mass communication Media, The Media, Mass Mass media Medios de comunicación de masas Price-earnings ratio Ratio C/B Coefficient de capitalisation des résultats CCR Coefficient de capitalisation des bénéfices RCB PER Multiple décapitalisation des résultats Multiple cours-bénéfice Régimes privés de retraite par capitalisation Plans d'épargne en vue de la retraite Plan d'épargne-retraite Comptes d'épargne-retraite Fonds de pension Régimes de retraite par capitalisation PER Retraite-épargne Media Techniques de diffusion collective Moyens de communication de masse Mass media Communication de masse Médias d'informationCiter ce document
Annalina Caputo et al., « Overview of the EVALITA 2016 Question Answering for Frequently Asked Questions (QA4FAQ) Task », Accademia University Press, ID : 10.4000/books.aaccademia.1970
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Résumé
This paper describes the first edition of the Question Answering for Frequently Asked Questions (QA4FAQ) task at the EVALITA 2016 campaign. The task concerns the retrieval of relevant frequently asked questions, given a user query. The main objective of the task is the evaluation of both question answering and information retrieval systems in this particular setting in which the document collection is composed of FAQs. The data used for the task are collected in a real scenario by AQP Risponde, a semantic retrieval engine used by Acquedotto Pugliese (AQP, the Organization for the management of the public water in the South of Italy) for supporting their customer care. The system is developed by QuestionCube, an Italian startup company which designs Question Answering tools.