Coupling Maximum Entropy and Probabilistic Context-Free Grammar Models for XML Annotation of Documents.
Boris Chidlovskii, Jérôme Fuselier · 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 generalized probabilistic contextfree 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 probable parse for the selected terminal sequence. We extend the inside-outside algorithm for probabilistic context-free grammars and establish a Naive Bayes-like requirement that the content classifier should satisfy when estimating the terminal probabilities. Nous considérons le problème de l’annotation sémantique de documents semistructurés guidée par un schéma xml cible. Le but est d’annoter un document