Named Entity Recognition (NER)
886 papers with code • 76 benchmarks • 122 datasets
Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. 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 )
Libraries
Use these libraries to find Named Entity Recognition (NER) models and implementationsSubtasks
- NER
- Nested Named Entity Recognition
- Chinese Named Entity Recognition
- Few-shot NER
- Few-shot NER
- Medical Named Entity Recognition
- Multilingual Named Entity Recognition
- Cross-Domain Named Entity Recognition
- Named Entity Recognition In Vietnamese
- Multi-modal Named Entity Recognition
- Zero-shot Named Entity Recognition (NER)
- Toponym Recognition
- Scientific Concept Extraction
- Multi-Grained Named Entity Recognition
Most implemented papers
A Unified MRC Framework for Named Entity Recognition
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
TENER: Adapting Transformer Encoder for Named Entity Recognition
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Few-NERD: A Few-Shot Named Entity Recognition Dataset
In this paper, we present Few-NERD, a large-scale human-annotated few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types.
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
CamemBERT: a Tasty French Language Model
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
The Natural Language Decathlon: Multitask Learning as Question Answering
Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.