Algorithms for Enumerating All Spanning Trees ofUndirected and Weighted Graphs
SIAM Journal on Computing
Guest Editors‘ Introduction: Machine Learning and Natural Language
Machine Learning - Special issue on natural language learning
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Ontology ranking based on the analysis of concept structures
Proceedings of the 3rd international conference on Knowledge capture
Towards light semantic processing for Question Answering
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Measuring Semantic Similarity between Named Entities by Searching the Web Directory
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
What do they think?: aggregating local views about news events and topics
Proceedings of the 17th international conference on World Wide Web
POP2.0: A search engine for public information services in local government
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Generic parsing for multi-domain semantic interpretation
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
From generalization of syntactic parse trees to conceptual graphs
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
Transfer learning of syntactic structures for building taxonomies for search engines
Engineering Applications of Artificial Intelligence
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We implement a scalable mechanism to build a taxonomy of entities which improves relevance of search engine in a vertical domain. Taxonomy construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Taxonomy and syntactic generalization is applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources.