IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Learning search engine specific query transformations for question answering
Proceedings of the 10th international conference on World Wide Web
Mining the web for answers to natural language questions
Proceedings of the tenth international conference on Information and knowledge management
On the MSE robustness of batching estimators
Proceedings of the 33nd conference on Winter simulation
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Extending a Lexical Ontology by a Combination of Distributional Semantics Signatures
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Text Mining for Causal Relations
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Probabilistic question answering on the Web: Research Articles
Journal of the American Society for Information Science and Technology
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Performance issues and error analysis in an open-domain Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Evaluation of resources for question answering evaluation
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Beyond keywords: Automated question answering on the web
Communications of the ACM - Enterprise information integration: and other tools for merging data
Detecting Word Substitutions in Text
IEEE Transactions on Knowledge and Data Engineering
A probabilistic model of redundancy in information extraction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Corpus-based thesaurus construction for image retrieval in specialist domains
ECIR'03 Proceedings of the 25th European conference on IR research
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Many artificial intelligence tasks, such as automated question answering, reasoning, or heterogeneous database integration, involve verification of a semantic category (e.g. ''coffee'' is a drink, ''red'' is a color, while ''steak'' is not a drink and ''big'' is not a color). In this research, we explore completely automated on-the-fly verification of a membership in any arbitrary category which has not been expected a priori. Our approach does not rely on any manually codified knowledge (such as WordNet or Wikipedia) but instead capitalizes on the diversity of topics and word usage on the World Wide Web, thus can be considered ''knowledge-light'' and complementary to the ''knowledge-intensive'' approaches. We have created a quantitative verification model and established (1) what specific variables are important and (2) what ranges and upper limits of accuracy are attainable. While our semantic verification algorithm is entirely self-contained (not involving any previously reported components that are beyond the scope of this paper), we have tested it empirically within our fact seeking engine on the well known TREC conference test questions. Due to our implementation of semantic verification, the answer accuracy has improved by up to 16% depending on the specific models and metrics used.