Instance-Based Learning Algorithms
Machine Learning
WordNet: a lexical database for English
Communications of the ACM
Verb paraphrase based on case frame alignment
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Text simplification for reading assistance: a project note
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Generation of referring expressions: managing structural ambiguities
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Hi-index | 0.00 |
We present a study that investigates that factors that determine what makes a good lexical substitution. We begin by observing that there is a correlation between the corpus frequency of words and the number of WordNet senses they have, and hypothesise that readers might prefer common, but more ambiguous words over less ambiguous but also less common ones. We identify four properties of a word that determine whether it is a suitable substitution in a given context, and ask volunteers to rank their preferences between two common but ambiguous lexical substitutions, and two uncommon but also unambiguous ones. Preliminary results suggest a slight preference towards the unambiguous.