{"type":"ScholarlyArticle","title":"A Probabilistic Learning Method for XML Annotation of Documents","authors":[{"name":"Boris Chidlovskii","slug":"boris-chidlovskii","given":"Boris","family":"Chidlovskii","affiliation":""},{"name":"Jérôme Fuselier","slug":"jerome-fuselier","given":"Jérôme","family":"Fuselier","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2005/29","pdf":"https://ijcai.org/proceedings/2005/0029.pdf","markdown":"https://ijcai.org/proceedings/2005/29.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/29","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":29,"pages":{"first":1016,"last":1020},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}