Named Entity Recognition
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.
Example:
| Mark | Watney | visited | Mars |
|---|---|---|---|
| B-PER | I-PER | O | B-LOC |
( Image credit: Zalando )
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Subtasks
Greatest papers with code
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL 2019
• tensorflow/models
• 
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
SOTA for Common Sense Reasoning on SWAG
COMMON SENSE REASONING CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION QUESTION ANSWERING SENTENCE CLASSIFICATION
Semi-Supervised Sequence Modeling with Cross-View Training
EMNLP 2018
• tensorflow/models
•
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.
SOTA for CCG Supertagging on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION UNSUPERVISED REPRESENTATION LEARNING
CamemBERT: a Tasty French Language Model
We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.
DEPENDENCY PARSING LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE PART-OF-SPEECH TAGGING
Deep contextualized word representations
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).
#2 best model for
Sentiment Analysis
on SST-5 Fine-grained classification
CITATION INTENT CLASSIFICATION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS
Semi-supervised sequence tagging with bidirectional language models
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
#20 best model for
Named Entity Recognition
on CoNLL 2003 (English)
Neural Architectures for Named Entity Recognition
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.
#32 best model for
Named Entity Recognition
on CoNLL 2003 (English)
Named Entity Recognition with Bidirectional LSTM-CNNs
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.
ENTITY LINKING FEATURE ENGINEERING NAMED ENTITY RECOGNITION WORD EMBEDDINGS
Pooled Contextualized Embeddings for Named Entity Recognition
NAACL 2019
• zalandoresearch/flair
• 
We make all code and pre-trained models available to the research community for use and reproduction.
#6 best model for
Named Entity Recognition
on CoNLL 2003 (English)
(using extra training data)
Contextual String Embeddings for Sequence Labeling
COLING 2018
• zalandoresearch/flair
•
Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.
SOTA for Chunking on Penn Treebank
CHUNKING LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS