Paper: Glen Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender

ACL ID W09-1116
Title Glen Glenda or Glendale: Unsupervised and Semi-supervised Learning of English Noun Gender
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2009
Authors

English pronouns like he and they reliably re- flect the gender and number of the entities to which they refer. Pronoun resolution systems can use this fact to filter noun candidates that do not agree with the pronoun gender. In- deed, broad-coverage models of noun gender have proved to be the most important source of world knowledge in automatic pronoun res- olution systems. Previous approaches predict gender by count- ing the co-occurrence of nouns with pronouns of each gender class. While this provides use- ful statistics for frequent nouns, many infre- quent nouns cannot be classified using this method. Rather than using co-occurrence in- formation directly, we use it to automatically annotate training examples for a large-scale discriminative gender model. Our model col- lectively cla...