Disambiguating Ambiguous Biomedical Terms in Biomedical Narrative Text: An Unsupervised Method

An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies inte...

Resolving Ambiguities in Biomedical Text With Unsupervised Clustering Approaches

This paper explores the effectiveness of unsupervised clustering techniques developed for general English in resolving semantic ambiguities in the biomedical domain. Methods that use first and second order representations of context are evaluated on the National Library of Medicine Word Sense Disambiguation Corpus. We show that the method of clustering second order contexts in similarity space ...

An unsupervised strategy for biomedical image segmentation

Many segmentation techniques have been published, and some of them have been widely used in different application problems. Most of these segmentation techniques have been motivated by specific application purposes. Unsupervised methods, which do not assume any prior scene knowledge can be learned to help the segmentation process, and are obviously more challenging than the supervised ones. In ...

Selecting an Ontology for Biomedical Text Mining

Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A...

Unsupervised Biomedical Named Entity Recognition