Sentence-Level Evaluation Using Co-occurences of N-Grams

Authors:
Theologos Athanaselis;Stelios Bakamidis;Konstantinos Mamouras;Ioannis Dologlou
Affiliations:
Institute for Language and Speech Processing, , Athens, Greece 15125;Institute for Language and Speech Processing, , Athens, Greece 15125;Institute for Language and Speech Processing, , Athens, Greece 15125;Institute for Language and Speech Processing, , Athens, Greece 15125
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

Citing 5
Cited 0

A statistical approach to machine translation

Computational Linguistics
An unsupervised method for detecting grammatical errors

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
How to detect grammatical errors in a text without parsing it

EACL '87 Proceedings of the third conference on European chapter of the Association for Computational Linguistics
Shake-and-bake machine translation

COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Shake-and-bake translation

COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work presents an evaluation method of Greek sentences with respect to word order errors. The evaluation method is based on words' reordering and choosing the version that maximizes the number of trigram hits according to a language model. The new parameter of the proposed technique concerns the incorporation of unigram probability. This probability corresponds to the frequency of each unigram to be posed in the first and in the last position of the training set sentences. The comparative advantage of this method is that it works with a large set of words, and avoids the laborious and costly process of collecting word order errors for creating error patterns.