Named Entity Recognition (NER)
879 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
Latest papers with no code
LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
Named Entity Recognition (NER) serves as a fundamental task in natural language understanding, bearing direct implications for web content analysis, search engines, and information retrieval systems.
Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies
Our results indicate that a strategic mix of distilled and original data markedly elevates the NER capabilities of BERT.
FaBERT: Pre-training BERT on Persian Blogs
We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts.
Named Entity Recognition for Address Extraction in Speech-to-Text Transcriptions Using Synthetic Data
This paper introduces an approach for building a Named Entity Recognition (NER) model built upon a Bidirectional Encoder Representations from Transformers (BERT) architecture, specifically utilizing the SlovakBERT model.
Graph Neural Network and NER-Based Text Summarization
With the abundance of data and information in todays time, it is nearly impossible for man, or, even machine, to go through all of the data line by line.
In-Context Learning for Few-Shot Nested Named Entity Recognition
In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address.
NanoNER: Named Entity Recognition for nanobiology using experts' knowledge and distant supervision
It highlighted the dependency of the approach to the resource, while also confirming its ability to rediscover up to 30% of the ablated terms.
Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness
We utilize well-established data collected in previous research from Twitter to assess the competitiveness of these chatbots when compared to specialized models trained for those tasks.
Multicultural Name Recognition For Previously Unseen Names
In order for downstream tasks to not exhibit bias based on cultural background, a model should perform well on names from a variety of backgrounds.
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition
This imbalance leads to misclassifications of the entity classes as the O-class.