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Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Ranked #16 on Named Entity Recognition on Ontonotes v5 (English)
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
Ranked #1 on Relation Extraction on FewRel
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Ranked #2 on Entity Disambiguation on TAC2010
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
We consider the zero-shot entity-linking challenge where each entity is defined by a short textual description, and the model must read these descriptions together with the mention context to make the final linking decisions.
The package cleanNLP provides a set of fast tools for converting a textual corpus into a set of normalized tables.
This paper presents the formal release of MedMentions, a new manually annotated resource for the recognition of biomedical concepts.