/aidanf/

Using the Semantic Web for Linking and Reusing Data Across Web 2.0 Communities

Bojars, U., Breslin, J. G. & Finn, A. (2007). Using the Semantic Web for Linking and Reusing Data Across Web 2.0 Communities. To appear Journal of Web Semantics (JWS), Special issue on Web 2.0 and the Semantic Web, (pdf not available yet - I'll put it here after the final version is submitted).

A Multi-Level Boundary Classification Approach to Information Extraction

Finn, A. (2006). A Multi-Level Boundary Classification Approach to Information Extraction. Phd thesis (University College Dublin). pdf

Abstract
Information Extraction (IE) is the process of identifying a set of pre-defined relevant items in text documents. We investigate the application of Machine Learning classification techniques to the problem of Information Extraction. In particular we use Support Vector Machines and several different feature-sets to build a set of classifiers for Information Extraction (IE). We show that this approach is competitive with current state-of-the-art Information Extraction algorithms based on specialized learning algorithms.

Learning to classify documents according to genre

Finn, A. & Kushmerick, N. (2006). Learning to classify documents according to genre. To appear Journal of the American Society for Information Science and Technology (JASIST), Special Issue on Computational Analysis of Style, volume 57, number 11, September 2006. pdf

Multi-level Boundary Classification for Information Extraction

Finn, A. & Kushmerick, N. (2004). Multi-level Boundary Classification for Information Extraction. In Proc. European Conference on Machine Learning (Pisa). pdf

Information Extraction by Convergent Boundary Classification

Finn, A. & Kushmerick, N. (2004). Information Extraction by Convergent Boundary Classification. AAAI-04 Workshop on Adaptive Text Extraction and Mining (San Jose).pdf

Active learning selection strategies for information extraction

Finn, A. & Kushmerick, N. (2003). Active learning selection strategies for information extraction. ECML-03 Workshop on Adaptive Text Extraction and Mining (Croatia).pdf

Learning to classify documents according to genre

Finn, A. & Kushmerick, N. (2003). Learning to classify documents according to genre. IJCAI-03 Workshop on Computational Approaches to Style Analysis and Synthesis (Acapulco).pdf, postscript

Active learning strategies for information extraction

Finn, A. & Kushmerick, N. (2003). Active learning strategies for information extraction. Poster submission(rejected) to International Joint Conference on Artificial Intelligence (Acapulco). pdf

Machine learning for genre classification

Finn, A. (2002). Machine learning for genre classification. Msc thesis (University College Dublin). postscript

Genre classification and domain transfer for information filtering

Finn, A., Kushmerick, N. & Smyth, B. (2002). Genre classification and domain transfer for information filtering. In Proc. European Colloquium on Information Retrieval Research (Glasgow). postscript

Fact or fiction: Content classification for digital libraries

Finn, A., Kushmerick, N., & Smyth, B. (2001). Fact or fiction: Content classification for digital libraries. Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries (Dublin). postscript, pdf