The three systems presented by IXA Group (HiTZ center) to the competition CAPITEL@IberLEF2020 have ranked first in Sub-task 1 (Named Entity Recognition and Classification in Spanish News Articles). The systems were developed by Rodrigo Agerri with the help of German Rigau, Ander Barrena and Jon Ander Campos.
Zorionak, congratulations to Rodrigo and all the team!
Within the framework of the PlanTL, the Royal Spanish Academy (RAE) and the Secretariat of State for Digital Advancement (SEAD) of the Ministry of Economy signed an agreement for developing a linguistically annotated corpus of Spanish news articles, aimed at expanding the language resource infrastructure for the Spanish language. The name of such corpus is CAPITEL (Corpus del Plan de Impulso a las Tecnologías del Lenguaje}, and is composed of contemporary news articles thanks to agreements with a number of news media providers. CAPITEL has three levels of linguistic annotation: morphosyntactic (with lemmas and Universal Dependencies-style POS tags and features), syntactic (following Universal Dependencies v2), and named entities.
The linguistic annotation of a subset of the CAPITEL corpus has been revised using a machine-annotation-followed-by-human-revision procedure. Manual revision has been carried out by a team of graduated linguists using the Annotation Guidelines created specifically for CAPITEL. The named entity and syntactic layers of revised annotations comprise about 1 million words for the former, and roughly 250,000 for the latter.
Due to the size of the corpus and the nature of the annotations, they proposed two IberLEF sub-tasks under the more general, umbrella task of CAPITEL @ IberLEF 2020, where they used the revised subset of the CAPITEL corpus in two challenges, namely:
(1) Named Entity Recognition and Classification and
(2) Universal Dependency Parsing.
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