|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
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.
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
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.
SOTA for Entity Disambiguation on TAC2010
The package cleanNLP provides a set of fast tools for converting a textual corpus into a set of normalized tables.
Currently, the best performing approaches rely on trained mono-lingual models.
Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base.