Generalized Mongue-Elkan Method for Approximate Text String Comparison
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Creating relational data from unstructured and ungrammatical data sources
Journal of Artificial Intelligence Research
Exploiting background knowledge to build reference sets for information extraction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
RankIE: document retrieval on ranked entity graphs
Proceedings of the VLDB Endowment
Harvesting relational tables from lists on the web
Proceedings of the VLDB Endowment
FOCIH: Form-Based Ontology Creation and Information Harvesting
ER '09 Proceedings of the 28th International Conference on Conceptual Modeling
Graph-based concept identification and disambiguation for enterprise search
Proceedings of the 19th international conference on World wide web
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Constructing reference sets from unstructured, ungrammatical text
Journal of Artificial Intelligence Research
Harvesting relational tables from lists on the web
The VLDB Journal — The International Journal on Very Large Data Bases
Building Mashups by Demonstration
ACM Transactions on the Web (TWEB)
Semi-supervised multi-task learning of structured prediction models for web information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
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Information extraction from unstructured, ungrammatical data such as classified listings is difficult because traditional structural and grammatical extraction methods do not apply. Previous work has exploited reference sets to aid such extraction, but it did so using supervised machine learning. In this paper, we present an unsupervised approach that both selects the relevant reference set(s) automatically and then uses it for unsupervised extraction. We validate our approach with experimental results that show our unsupervised extraction is competitive with supervised machine learning approaches, including the previous supervised approach that exploits reference sets.