The use of BERT, one of the most popular language models, has led to improvements in
many Natural Language Processing (NLP) tasks. One such task is Named Entity …

Transformer-based contextualized language models constitute the state-of-the-art in several
natural language processing (NLP) tasks and applications. Despite their utility …

In this paper, we present an entity-centric infrastructure to manage legal documents,
especially court judgments, based on the organization of a textual document repository and …

The Holocaust was a tragic and catastrophic event in World War II (WWII) history that
resulted in the loss of millions of lives. In recent years, the emergence of the field of digital …

The extraction of named entities from court judgments is useful in several downstream
applications, such as document anonymization and semantic search engines. In this paper …

Natural Language Processing (NLP) based approaches have recently become very popular
for studies in legal domain. In this work, the outcomes of the cases of the Court of Cassation …

Named entity recognition (NER) plays a pivotal role in Natural Language Processing by
identifying and classifying entities within textual data. While NER methodologies have seen …

Задача распознавания именованных сущностей (named entity recognition, NER)
состоит в выделении и классификации слов и словосочетаний, обозначающих …

Transformer-based models have achieved remarkable success in various Natural Language
Processing (NLP) tasks, yet their ability to handle long documents is constrained by …

This work introduces the first prior case retrieval models for Turkish courts. We investigated
the rulings of the Court of Cassation of Turkey. Since law professionals have to find texts …