Identification of named entities from legal texts is an essential building block for developing
other legal Artificial Intelligence applications. Named Entities in legal texts are slightly …

This paper describes an approach at Named Entity Recognition (NER) in German language
documents from the legal domain. For this purpose, a dataset consisting of German court …

We present a multilingual Named Entity Recognition approach based on a robust and
general set of features across languages and datasets. Our system combines shallow local …

We describe a dataset developed for Named Entity Recognition in German federal court
decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource …

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 …

We ask how to practically build a model for German named entity recognition (NER) that
performs at the state of the art for both contemporary and historical texts, ie, a big-data and a …

Off-the-shelf part-of-speech taggers typically perform relatively poorly on web and social
media texts since those domains are quite different from the newspaper articles on which …

The vast amount of social communication distributed over various electronic media channels
(tweets, blogs, emails, etc.), so-called user-generated content (UGC), creates entirely new …

In this article, we demonstrate the impact of interactive machine learning: we develop
biomedical entity recognition dataset using a human-into-the-loop approach. In contrary to …

In this paper, we propose a novel way of improving named entity recognition (NER) in the
Korean language using its language-specific features. While the field of NER has been …