Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Extracting the names of genes and gene products with a hidden Markov model
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
MUC5 '93 Proceedings of the 5th conference on Message understanding
A Probabilistic Model for Identifying Protein Names and their Name Boundaries
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
BioMap: toward the development of a knowledge base of biomedical literature
Proceedings of the 2004 ACM symposium on Applied computing
Growing adaptation of computer science in Bioinfomatics
ISICT '04 Proceedings of the 2004 international symposium on Information and communication technologies
A hybrid approach to protein name identification in biomedical texts
Information Processing and Management: an International Journal
Identifying synonymous concepts in preparation for technology mining
Journal of Information Science
@Note: A workbench for Biomedical Text Mining
Journal of Biomedical Informatics
Biomedical association mining and validation
ISB '10 Proceedings of the International Symposium on Biocomputing
A text-mining technique for extracting gene-disease associations from the biomedical literature
International Journal of Bioinformatics Research and Applications
Relation-Based document retrieval for biomedical literature databases
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Relation-Based document retrieval for biomedical IR
Transactions on Computational Systems Biology V
Using concept-based indexing to improve language modeling approach to genomic IR
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Accurate and computationally efficient approaches in discovering relationships between biological objects from text documents are important for biologists to develop biological models. This paper presents a novel approach to extract relationships between multiplebiological objects that are present in a text document. The approach involves object identification, reference resolution, ontology and synonym discovery, and extracting object-object relationships. Hidden Markov Models (HMMs), dictionaries, and N-Gram models are used to set the framework to tackle the complex task of extracting object-object relationships. Experiments were carried out using a corpus of one thousand Medline abstracts. Intermediate results were obtained for the object identification process, synonym discovery, and finally the relationship extraction. For a corpus of thousand abstracts, 53 relationships were extracted of which 43 were correct, giving a specificity of 81%. The approach is both adaptable and scalable to new problems as opposed to rule-based methods.