Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Conceptual-model-based data extraction from multiple-record Web pages
Data & Knowledge Engineering
Hierarchical Wrapper Induction for Semistructured Information Sources
Autonomous Agents and Multi-Agent Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
RoadRunner: Towards Automatic Data Extraction from Large Web Sites
Proceedings of the 27th International Conference on Very Large Data Bases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Unstructured Data into Relational Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Unsupervised learning of field segmentation models for information extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
KnowItNow: fast, scalable information extraction from the web
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
International Journal on Document Analysis and Recognition
Automatic Taxonomy Extraction Using Google and Term Dependency
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Information extraction from Wikipedia: moving down the long tail
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unsupervised information extraction approach using graph mutual reinforcement
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Creating relational data from unstructured and ungrammatical data sources
Journal of Artificial Intelligence Research
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Semantic annotation of unstructured and ungrammatical text
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Exploiting background knowledge to build reference sets for information extraction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Principal components for automatic term hierarchy building
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Discovering a term taxonomy from term similarities using principal component analysis
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
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Vast amounts of text on the Web are unstructured and ungrammatical, such as classified ads, auction listings, forum postings, etc. We call such text "posts." Despite their inconsistent structure and lack of grammar, posts are full of useful information. This paper presents work on semi-automatically building tables of relational information, called "reference sets," by analyzing such posts directly. Reference sets can be applied to a number of tasks such as ontology maintenance and information extraction. Our reference-set construction method starts with just a small amount of background knowledge, and constructs tuples representing the entities in the posts to form a reference set. We also describe an extension to this approach for the special case where even this small amount of background knowledge is impossible to discover and use. To evaluate the utility of the machine-constructed reference sets, we compare them to manually constructed reference sets in the context of reference-set-based information extraction. Our results show the reference sets constructed by our method outperform manually constructed reference sets. We also compare the reference-set-based extraction approach using the machine-constructed reference set to supervised extraction approaches using generic features. These results demonstrate that using machine-constructed reference sets outperforms the supervised methods, even though the supervised methods require training data.