The aim of this work is to study the impact of the COVID-19 pandemic on the Basque speaking Twitter community by applying Natural Language Processing unsupervised techniques. In order to carry out this study, we collected and publicly released the biggest dataset of Basque tweets containing up to 8M tweets from September 2019 to February 2021. To analyze the impact of the pandemic, the variability of the content over time was studied through quantitative and qualitative analysis of words and emojis. For the quantitative analysis, the shift at the frequency of the terms was calculated using linear regression over frequencies. On the other hand, for the qualitative analysis, word embeddings were used to study the changes in the meaning of the most significant words and emojis at different periods of the pandemic. Through this multifaceted approach, we discovered noteworthy alterations in the political inclinations exhibited by Basque users throughout the course of the pandemic.

Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER systems face, such as having to classify emerging or complex entities in a fine-grained way. In this paper we present a novel NER cascade approach comprising three steps: first, identifying candidate entities in the input sentence; second, linking the each candidate to an existing knowledge base; third, predicting the fine-grained category for each entity candidate. We empirically demonstrate the significance of external knowledge bases in accurately classifying fine-grained and emerging entities. Our system exhibits robust performance in the MultiCoNER2 shared task, even in the low-resource language setting where we leverage knowledge bases of high-resource languages.