Recognition of named entities present in text is an important step towards information
extraction and natural language understanding. This work presents a named entity …

In our research we have created a text summarization software tool for Hungarian using
multilingual and Hungarian BERT-based models. Two types of text summarization method …

This article presents the current outcomes of the CURLICAT CEF Telecom project, which
aims to collect and deeply annotate a set of large corpora from selected domains. The …

The amount of legislative documents produced within the past decade has risen
dramatically, making it difficult for law practitioners to consult and update legislation. Named …

The increasing use of artificial intelligence methods in the legal field has sparked interest in
applying Natural Language Processing techniques to handle legal tasks and reduce the …

Research in the field of language models is rapidly evolving, with many open models being
released to the public. Openly available pretraining corpora usually focus on only a handful …

This work introduces HistNERo, the first Romanian corpus for Named Entity Recognition
(NER) in historical newspapers. The dataset contains 323k tokens of text, covering more …

The availability of annotated legal corpora is crucial for a number of tasks, such as legal
search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be …

This paper presents the methodology and data used for the automatic extraction of the
Romanian Academic Word List (Ro-AWL). Academic Word Lists are useful in both L2 and L1 …

With the increased size of Universal Dependencies tree banks, several basic language
processing kits (BLARK) for multiple languages appeared in recent years, indicating …