sisinflab: an ensemble of supervised and unsupervised strategies for the NEEL-IT challenge at Evalita 2016
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28 août 2017
- doi: 10.4000/books.aaccademia.1952
- eisbn: 9788899982553
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
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
Learning process User-generated media Scholarship LearningCiter ce document
Vittoria Cozza et al., « sisinflab: an ensemble of supervised and unsupervised strategies for the NEEL-IT challenge at Evalita 2016 », Accademia University Press, ID : 10.4000/books.aaccademia.1952
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Résumé
This work presents the solution adopted by the sisinflab team to solve the task NEEL-IT (Named Entity rEcognition and Linking in Italian Tweets) at the Evalita 2016 challenge. The task consists in the annotation of each named entity mention in a Twitter message written in Italian, among characters, events, people, locations, organizations, products and things and the eventual linking when a corresponding entity is found in a knowledge base (e.g. DBpedia). We faced the challenge through an approach that combines unsupervised methods, such as DBpedia Spotlight and word embeddings, and supervised techniques such as a CRF classifier and a Deep learning classifier.