A probabilistic learning method for XML annotation of documents
Boris Chidlovskii and Jérôme Fuselier
In: IJCAI 2005, Edinburgh, UK(2005).

Abstract

We consider the problem of semantic annotation of semi-structured documents according to a target XML schema. The task is to annotate a document in a tree-like manner where the annotation tree is an instance of a tree class defined by DTD or W3C XML Schema descriptions. In the probabilistic setting, we cope with the tree annotation problem as a {\it generalized probabilistic context-free parsing} of an observation sequence where each observation comes with a probability distribution over terminals supplied by a probabilistic classifier associated with the content of documents. We determine the most probable tree annotation by maximizing the joint probability of selecting a terminal sequence for the observation sequence and the most probableparse for the selected terminal sequence.

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