Few-NERD: A Few-Shot Named Entity Recognition Dataset

by   Ning Ding, et al.

Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few examples, while in practice, most unseen entity types are fine-grained. 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. Few-NERD consists of 188,238 sentences from Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge, this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that Few-NERD is challenging and the problem requires further research. We make Few-NERD public at https://ningding97.github.io/fewnerd/.


page 1

page 5


Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers

Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNE...

Building a Massive Corpus for Named Entity Recognition using Free Open Data Sources

With the recent progress in machine learning, boosted by techniques such...

TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis

This technique report introduces TexSmart, a text understanding system t...

A Pragmatic Guide to Geoparsing Evaluation

Empirical methods in geoparsing have thus far lacked a standard evaluati...

CLUENER2020: Fine-grained Name Entity Recognition for Chinese

In this paper, we introduce the NER dataset from CLUE organization (CLUE...

Zero-Shot Open Entity Typing as Type-Compatible Grounding

The problem of entity-typing has been studied predominantly in supervise...

Fine-grained Entity Recognition with Reduced False Negatives and Large Type Coverage

Fine-grained Entity Recognition (FgER) is the task of detecting and clas...