Learning Random Walk Models for Inducing Word Dependency Distributions (2004)
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| Venue: | IN ICML |
| Citations: | 39 - 0 self |
BibTeX
@INPROCEEDINGS{Toutanova04learningrandom,
author = {Kristina Toutanova and Christopher D. Manning and Andrew Y. Ng},
title = {Learning Random Walk Models for Inducing Word Dependency Distributions},
booktitle = {IN ICML},
year = {2004},
publisher = {ACM Press}
}
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Abstract
Many NLP tasks rely on accurately estimating word dependency probabilities P(w 1 |w 2 ), where the words w 1 and w 2 have a particular relationship (such as verb-object). Because of the sparseness of counts of such dependencies, smoothing and the ability to use multiple sources of knowledge are important challenges. For example, if the probability P(N ) of noun N being the subject of verb V is high, and V takes similar objects to V # , and V # is synonymous to V ## , then we want to conclude that P(N ## ) should also be reasonably high---even when those words did not cooccur in the training data. To capture
