DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Class-based probability estimation using a semantic hierarchy
Computational Linguistics
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
AI Magazine
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Extracting regulatory gene expression networks from PubMed
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Classifying semantic relations in bioscience texts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Automatically Harvesting and Ontologizing Semantic Relations
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Dependency parsing with second-order feature maps and annotated semantic information
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning Expressive Ontologies
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Concept and relation extraction in the finance domain
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Refining non-taxonomic relation labels with external structured data to support ontology learning
Data & Knowledge Engineering
Deriving clinical query patterns from medical corpora using domain ontologies
WBIE '09 Proceedings of the Workshop on Biomedical Information Extraction
Learning semantic n-ary relations from Wikipedia
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Unsupervised ontology acquisition from plain texts: the OntoGain system
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Automated construction of domain ontology taxonomies from wikipedia
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
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Manual ontology building in the biomedical domain is a work-intensive task requiring the participation of both domain and knowledge representation experts. The representation of biomedical knowledge has been found of great use for biomedical text mining and integration of biomedical data. In this chapter we present an unsupervised method for learning arbitrary semantic relations between ontological concepts in the molecular biology domain. The method uses the GENIA corpus and ontology to learn relations between annotated named-entities by means of several standard natural language processing techniques. An in-depth analysis of the output evaluates the accuracy of the model and its potentials for text mining and ontology building applications. The proposed learning method does not require domain-specific optimization or tuning and can be straightforwardly applied to arbitrary domains, provided the basic processing components exist.