An algorithm for pronominal anaphora resolution
Computational Linguistics
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
WWW '03 Proceedings of the 12th international conference on World Wide Web
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Discovering informative connection subgraphs in multi-relational graphs
ACM SIGKDD Explorations Newsletter
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Classifying semantic relations in bioscience texts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Recognising nested named entities in biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
A context pattern induction method for named entity extraction
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A framework for schema-driven relationship discovery from unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Relationship Web: Spinning the Web from Trailblazing to Semantic Analytics
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Semantics-empowered text exploration for knowledge discovery
Proceedings of the 48th Annual Southeast Regional Conference
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In this paper we investigate unsupervised population of a biomedical ontology via information extraction from biomedical literature. Relationships in text seldom connect simple entities. We therefore focus on identifying compound entities rather than mentions of simple entities. We present a method based on rules over grammatical dependency structures for unsupervised segmentation of sentences into compound entities and relationships. We complement the rule-based approach with a statistical component that prunes structures with low information content, thereby reducing false positives in the prediction of compound entities, their constituents and relationships. The extraction is manually evaluated with respect to the UMLS Semantic Network by analyzing the conformance of the extracted triples with the corresponding UMLS relationship type definitions.