Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
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We describe a dataset developed for Named Entity Recognition in German federal court decisions.
However, wrong combination of hyper-parameters can produce poor quality vectors.
We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.
The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer.
Named Entity Recognition (NER) has greatly advanced by the introduction of deep neural architectures.
Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus.
SOTA for Sentiment Analysis on HARD
For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms.