Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning

Details Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning

1996-12-09

arxiv.org/abs/cmp-lg/9612001v1

Computer Science

Computation and Language

10 pages

Scientific paper

This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The specific problem tested involves disambiguating six senses of the word ``line'' using the words in the current and proceeding sentence as context. The statistical and neural-network methods perform the best on this particular problem and we discuss a potential reason for this observed difference. We also discuss the role of bias in machine learning and its importance in explaining performance differences observed on specific problems.

Affiliated with

Also associated with

No associations

Rating

Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-633278