A Maximum Entropy Classifier for Cross-Lingual Pronoun Prediction

Dominikus Wetzel, Adam Lopez, Bonnie Webber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a maximum entropy classifier for cross-lingual pronoun prediction. The features are based on local source- and target-side contexts and antecedent information obtained by a co-reference resolution system. With only a small set of feature types our best performing system achieves an accuracy of 72.31%. According to the shared task’s official macro- averaged F1-score at 57.07%, we are among the top systems, at position three out of 14. Feature ablation results show the important role of target-side information in general and of the resolved target-side antecedent in particular for predicting the correct classes.
Original languageEnglish
Title of host publicationProceedings of the Second Workshop on Discourse in Machine Translation
Place of PublicationLisbon, Portugal
PublisherAssociation for Computational Linguistics
Pages115-121
Number of pages7
Publication statusPublished - 1 Sep 2015

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