Document annotation by active learning techniques

Authors:
Loïc Lecerf;Boris Chidlovskii
Affiliations:
Xerox Research Centre;Xerox Research Centre
Venue:
Proceedings of the 2006 ACM symposium on Document engineering
Year:
2006

Citing 5
Cited 0

A maximum entropy approach to natural language processing

Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Diverse ensembles for active learning

ICML '04 Proceedings of the twenty-first international conference on Machine learning
From legacy documents to XML: a conversion framework

ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a system for the semantic annotation of layout-oriented documents, with an integrated learning component. We introduce probabilistic learning methods on tree-like documents and we present different active learning techniques for training document annotation models. We report some preliminary results of deploying such active learning techniques on an important case of document collection annotation.