Pith. sign in

REVIEW

Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2311.09122 v3 pith:GPNPCGFL submitted 2023-11-15 cs.CL

Stephen Mayhew , Terra Blevins , Shuheng Liu , Marek Šuppa , Hila Gonen , Joseph Marvin Imperial , Börje F. Karlsson , Peiqin Lin
show 5 more authors
Nikola Ljubešić LJ Miranda Barbara Plank Arij Riabi Yuval Pinter
This is my paper
classification cs.CL
keywords unerconsistentcross-lingualgold-standardlanguagesmultilingualnameduniversal
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public.

Discussion (0). Sign in to comment.

Pith tools