Paper: Word Sense Disambiguation Using Static And Dynamic Sense Vectors

ACL ID C02-1097
Title Word Sense Disambiguation Using Static And Dynamic Sense Vectors
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002
Authors

It is popular in WSD to use contextual information in training sense tagged data. Co-occurring words within a limited window-sized context support one sense among the semantically ambiguous ones of the word. This paper reports on word sense disambiguation of English words using static and dynamic sense vectors. First, context vectors are constructed using contextual words 1 in the training sense tagged data. Then, the words in the context vector are weighted with local density. Using the whole training sense tagged data, each sense of a target word 2 is represented as a static sense vector in word space, which is the centroid of the context vectors. Then contextual noise is removed using a automatic selective sampling. A automatic selective sampling method use information retrieval techniq...