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 therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
Ranked #1 on CCG Supertagging on CCGBank
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on Dependency Parsing on Spoken Corpus
We make all code and pre-trained models available to the research community for use and reproduction.
Ranked #8 on Named Entity Recognition on CoNLL 2003 (English) (using extra training data)
Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Ranked #24 on Named Entity Recognition on CoNLL 2003 (English)
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.
Ranked #39 on Named Entity Recognition on CoNLL 2003 (English)
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 #17 on Named Entity Recognition on Ontonotes v5 (English)