A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

This survey presents a comprehensive description of recent neural entity linking (EL)
systems developed since 2015 as a result of the “deep learning revolution” in natural …

We propose a new formulation for multilingual entity linking, where language-specific
mentions resolve to a language-agnostic Knowledge Base. We train a dual encoder in this …

Traditional named entity recognition models use gazetteers (lists of entities) as features to
improve performance. Although modern neural network models do not require such hand …

Wikidata is a frequently updated, community-driven, and multilingual knowledge graph.
Hence, Wikidata is an attractive basis for Entity Linking, which is evident by the recent …

Abstract Domain pretraining followed by task fine-tuning has become the standard paradigm
for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we …

Entity Linking is one of the most common Natural Language Processing tasks in practical
applications, but so far efficient end-to-end solutions with multilingual coverage have been …

Digital libraries have a key role in cultural heritage as they provide access to our culture and
history by indexing books and historical documents (newspapers and letters). Digital …

The disparity in the languages commonly studied in Natural Language Processing (NLP) is
typically reflected by referring to languages as low vs high-resourced. However, there is …

Biomedical named entity recognition and entity linking are important building blocks for
various clinical applications and downstream NLP tasks. In the clinical domain, language …