Automatically determining an anonymous author's native language

Authors:
Moshe Koppel;Jonathan Schler;Kfir Zigdon
Affiliations:
Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel;Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel;Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel
Venue:
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Year:
2005

Citing 6
Cited 3

Text Categorization with Suport Vector Machines: Learning with Many Relevant Features

ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines

The Journal of Machine Learning Research
Augmenting Naive Bayes Classifiers with Statistical Language Models

Information Retrieval
An unsupervised method for detecting grammatical errors

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A simple rule-based part of speech tagger

ANLC '92 Proceedings of the third conference on Applied natural language processing
You're not from 'round here, are you?: naive Bayes detection of non-native utterance text

NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies

Using classifier features for studying the effect of native language on the choice of written second language words

CACLA '07 Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition
Exploiting parse structures for native language identification

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Exploring adaptor grammars for native language identification

EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Text authored by an unidentified assailant can offer valuable clues to the assailant's identity. In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy.