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UNIMIB@NEEL-IT : Named entity recognition and linking of Italian tweets

Authors :
Cezar Sas
Pikakshi Manchanda
Debora Nozza
Matteo Palmonari
Enza Messina
Elisabetta Fersini
Flavio Massimiliano Cecchini
Semeraro G.,Sprugnoli R.,Corazza A.,Nissim M.,Basile P.,Montemagni S.,Patti V.,Cutugno F.
Cecchini, F
Fersini, E
Manchanda, P
Messina, V
Nozza, D
Palmonari, M
Sas, C
Source :
Scopus-Elsevier, CLiC-it/EVALITA
Publication Year :
2016
Publisher :
CEUR-WS, 2016.

Abstract

This paper describes the framework proposed by the UNIMIB Team for the task of Named Entity Recognition and Linking of Italian tweets (NEEL-IT). The proposed pipeline, which represents an entry level system, is composed of three main steps: (1) Named Entity Recognition using Conditional Random Fields, (2) Named Entity Linking by considering both Supervised and Neural-Network Language models, and (3) NIL clustering by using a graph-based approach. Questo articolo descrive il sistema proposto dal gruppo UNIMIB per il task di Named Entity Recognition and Linking applicato a tweet in lingua italiana (NEEL-IT). Il sistema, che rappresenta un approccio iniziale al problema, è costituito da tre passaggi fondamentali: (1) Named Entity Recognition tramite l’utilizzo di Conditional Random Fields, (2) Named Entity Linking considerando sia approcci supervisionati sia modelli di linguaggio basati su reti neurali, e (3) NIL clustering tramite un approccio basato su grafi.

Details

Language :
English
Database :
OpenAIRE
Journal :
Scopus-Elsevier, CLiC-it/EVALITA
Accession number :
edsair.doi.dedup.....c12697e55576526a70cf2cf22d967f04