Knowledge based word-concept model estimation and refinement for biomedical text mining

Ontology based Text Mining of Concept Definitions in Biomedical Literature

Many developers of biomedical knowledge bases typically validate and update formalized knowledge based on reviews of full-text scientific articles, but finding text relevant to domain concepts can be tedious and prone to errors. Prior methods have automated this process by matching term-based patterns within a single sentence. In our work developing a knowledge base of autism phenotypes, specif...

Effective Concept-Based Mining Model For Text Clustering

The common techniques in text mining are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. Two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Usually in text mining techniques the basic me...

A Fuzzy Similarity Based Concept Mining Model for Text Classification

Text Classification is a challenging and a red hot field in the current scenario and has great importance in text categorization applications. A lot of research work has been done in this field but there is a need to categorize a collection of text documents into mutually exclusive categories by extracting the concepts or features using supervised learning paradigm and different classification ...

Knowledge Sources for Word Sense Disambiguation of Biomedical Text

Like text in other domains, biomedical documents contain a range of terms with more than one possible meaning. These ambiguities form a significant obstacle to the automatic processing of biomedical texts. Previous approaches to resolving this problem have made use of a variety of knowledge sources including linguistic information (from the context in which the ambiguous term is used) and domai...

Text Mining Biomedical Literature for Genomic Knowledge Discovery