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

Reasoning about semantic classes and determining compatibility of the words in a given context is an important procedure used in many modules of natural language understanding systems. However, most existing systems do not devote much attention to their ontological knowledge representations, resulting in implementations that are not portable to other domains. At the same time, statistical methods are more robust and less labor-intensive to develop, but typically result in models that are not easily interpretable by humans. We propose a semantic feature representation the use in practical dialogue systems and argue that it can oer advantages in terms of lexicon development and portability - in particular for dening selectional restrictions - and can also be useful for other system modules that do logical inference. We then propose to develop statistical methods allowing us to learn parts of our representation from corpus data. The author wishes to thank James Allen, Jason Eisner, Len...

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.