---
title: "A Probabilistic Learning Method for XML Annotation of Documents"
authors:
  - "Boris Chidlovskii"
  - "Jérôme Fuselier"
doi: ""
url: "https://ijcai.org/proceedings/2005/29"
pdf: "https://ijcai.org/proceedings/2005/0029.pdf"
venue: "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence"
year: 2005
pages: "1016-1020"
track: ""
keywords:
---

# A Probabilistic Learning Method for XML Annotation of Documents

Boris Chidlovskii, Jérôme Fuselier

*Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence*, pages 1016-1020.

## 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 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 probable parse for the selected terminal sequence.