NER
578 papers with code • 6 benchmarks • 24 datasets
The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
Benchmarks
These leaderboards are used to track progress in NER
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Libraries
Use these libraries to find NER models and implementationsDatasets
MNIST
CoNLL 2003
Latest papers
Seeing Motion at Nighttime with an Event Camera
In this work, we present a novel nighttime dynamic imaging method with an event camera.
Intent Detection and Entity Extraction from BioMedical Literature
Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature.
BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering
Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner.
On-the-fly Definition Augmentation of LLMs for Biomedical NER
In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly.
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
csu-signal/xlingual-robustness
•
•
Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language.
ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition
In a zero-shot setting, ELLEN also achieves over 75% of the performance of a strong, fully supervised model trained on gold data.
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Then a superposition instance retriever is applied to retrieve corresponding instances of these superposition concepts from large-scale text corpus.
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution
We then propose a two-step neural mention and coreference resolution system, named SPLICE, and compare its performance to the end-to-end approach in two scenarios: the OntoNotes test set and the out-of-domain (OOD) OntoGUM corpus.
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects.
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation
We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network with a pre-trained image network in an unsupervised man- ner.
