Identification of non-referential zero pronouns for Korean-English machine translation

Authors:
Kye-Sung Kim;Seong-Bae Park;Hyun-Je Song;Se Young Park;Sang-Jo Lee
Affiliations:
Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea;Department of Computer Engineering, Kyungpook National University, Daegu, Korea
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

Citing 12
Cited 0

Centering: a framework for modeling the local coherence of discourse

Computational Linguistics
Making large-scale support vector machine learning practical

Advances in kernel methods
A machine learning approach to coreference resolution of noun phrases

Computational Linguistics - Special issue on computational anaphora resolution
Anaphora resolution by antecedent identification followed by anaphoricity determination

ACM Transactions on Asian Language Information Processing (TALIP)
Korean zero pronouns: analysis and resolution

Korean zero pronouns: analysis and resolution
Generation of Zero Pronouns Based on the Centering Theory and Pairwise Salience of Entities

IEICE - Transactions on Information and Systems
Learning noun phrase anaphoricity to improve coreference resolution: issues in representation and optimization

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Zero-anaphora resolution by learning rich syntactic pattern features

ACM Transactions on Asian Language Information Processing (TALIP)
A twin-candidate model for learning-based anaphora resolution

Computational Linguistics
Identification of Subject Shareness for Korean-English Machine Translation

PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Zero anaphora resolution by case-based reasoning and pattern conceptualization

Expert Systems with Applications: An International Journal
Capturing salience with a trainable cache model for zero-anaphora resolution

ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

The common use of null arguments is one of the most critical issues in pro-drop languages. When translating Korean into other languages, the omitted elements should be replaced with appropriate pronouns to get grammatical target sentences. One of the most important issues when dealing with zero pronouns is to determine the referentiality of zero pronouns. Since, like expletive 'it' in English, omitted elements do not have always explicit referents, it is important to determine whether a zero pronoun is referential or not. In this paper, we focus on identifying non-referential zero pronouns. Since non-referential zero pronouns are likely to occur in similar contexts, referentiality determination in this paper is regarded as the identification of clauses containing non-referential zero pronouns. Our method outperforms the baseline systems using n-grams and bag of words, and achieves the F-measure of 0.51 and 0.78.