DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic Pattern-Taxonomy Extraction for Web Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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
OntoCAPE-A large-scale ontology for chemical process engineering
Engineering Applications of Artificial Intelligence
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Pattern-based automatic taxonomy learning from the Web
AI Communications
Engineering Applications of Artificial Intelligence
A syntactic tree matching approach to finding similar questions in community-based qa services
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Generic parsing for multi-domain semantic interpretation
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
A methodology to learn ontological attributes from the Web
Data & Knowledge Engineering
From generalization of syntactic parse trees to conceptual graphs
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Using generalization of syntactic parse trees for taxonomy capture on the web
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Engineering Applications of Artificial Intelligence
Assessing plausibility of explanation and meta-explanation in inter-human conflicts
Engineering Applications of Artificial Intelligence
Generating and evaluating triples for modelling a virtual environment
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
Inferring the semantic properties of sentences by mining syntactic parse trees
Data & Knowledge Engineering
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
An automatic approach for ontology-based feature extraction from heterogeneous textualresources
Engineering Applications of Artificial Intelligence
Survey of social search from the perspectives of the village paradigm and online social networks
Journal of Information Science
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We apply a paradigm of transfer learning to build a taxonomy of entities intended to improve search engine relevance in a vertical domain. The taxonomy construction process starts from the seed entities and mines available source domains for new entities associated with these seed entities. New entities are formed by applying the machine learning of syntactic parse trees (their generalizations) to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration. To match natural language expressions between source and target domains, we use syntactic generalization, an operation which finds a set of maximal common sub-trees of constituency parse trees of these expressions. Taxonomy and syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that a proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. The proposed algorithm is implemented as a component of Apache OpenNLP project.