CLASSER: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition

CLASSER is a framework for cross-lingual annotation projection with script-similarity-based refinement to create high-quality fine-grained named entity recognition datasets.

Utilizing CLASSER, fine-grained named entity recognition dataset is created in five languages: Assamese (as), Bodo (brx), Marathi (mr), Nepali (ne) and Sanskrit (sa).

CLASSER Framework Overview

Figure: Overview of the CLASSER framework.

CLASSER Dataset Statictics

Language	Train set			Development set			Test set
	Sentences	Entities	Tokens	Sentences	Entities	Tokens	Sentences	Entities	Tokens	IAA (κ)
Assamese (as)	140,257	204,611	1,972,697	15,585	15,763	219,114	1,000	1,407	14,270	0.901
Bodo (brx)	212,835	302,713	2,958,455	23,649	33,808	329,145	1,000	1,423	14,082	0.875
Marathi (mr)	611,902	889,217	8,135,813	67,990	97,943	948,020	1,000	1,443	13,996	0.887
Nepali (ne)	414,561	617,957	5,531,683	46,062	64,098	642,489	1,000	1,436	14,142	0.882
Sanskrit (sa)	265,114	378,287	3,488,871	29,458	40,589	377,306	1,000	1,412	12,925	0.861

Note: IAA (Inter-Annotator Agreement) scores are represented using Cohen's κ.

Citation

If you use this dataset, please cite the following paper:

@inproceedings{kaushik2025classer,
  title     = {{CLASSER}: Cross-lingual Annotation Projection enhancement through Script Similarity for Fine-grained Named Entity Recognition},
  author    = {Kaushik, Prachuryya and Anand, Ashish},
  booktitle = {Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
  year      = {2025},
  publisher = {Association for Computational Linguistics},
  note      = {Main conference paper}
}