NE recognition without training data on a language you don't speak

Authors:
Diana Maynard;Valentin Tablan;Hamish Cunningham
Affiliations:
University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK
Venue:
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
Year:
2003

Citing 5
Cited 3

An Algorithm that Learns What‘s in a Name

Machine Learning - Special issue on natural language learning
Adapting a Robust Multi-genre NE System for Automatic Content Extraction

AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
A simple rule-based part of speech tagger

ANLC '92 Proceedings of the third conference on Applied natural language processing
A statistical profile of the Named Entity task

ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Multilingual adaptations of ANNIE, a reusable information extraction tool

EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2

Surprise! What's in a Cebuano or Hindi Name?

ACM Transactions on Asian Language Information Processing (TALIP)
Named entity recognition for Hungarian using various machine learning algorithms

Acta Cybernetica
A survey of methods to ease the development of highly multilingual text mining applications

Language Resources and Evaluation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we describe an experiment to adapt a named entity recognition system from English to Cebuano as part of the TIDES surprise language program. With 4 person-days of effort, and with no previous knowledge of which language would be involved, no knowledge of the language in question once it was announced, and no training data available, we adapted the ANNIE system for Cebuano and achieved an F-measure of 77.5%.