The volume of biomedical text is growing at a fast rate, creating challenges for humans and
computer systems alike. One of these challenges arises from the frequent use of novel …

Technological advances in biomedical research are generating a plethora of heterogeneous
data at a high rate. There is a critical need for extraction, integration and management tools …

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation
of scientific literature. However, most efforts have addressed the benchmarking of new …

Discovering significant relationships between biological entities from text documents is an
important task for biologists in order to develop biological models for research and …

In this research, the development of aconcept-clumping algorithm'designed to improve the
clustering of technical concepts is demonstrated. The algorithm developed first identifies a …

This paper proposes a method for identifying protein names in biomedical texts with an
emphasis on detecting protein name boundaries. We use a probabilistic model which …

We propose a new text mining technique to identify associations between biological entities,
specifically genes-diseases associations, from the biomedical literature. The proposed …

This paper presents a hybrid approach to identifying protein names in biomedical texts,
which is regarded as a crucial step for text mining. Our approach employs a set of simple …

Genomic IR, characterized by its highly specific information need, severe synonym and
polysemy problem, long term name and rapid growing literature size, is challenging IR …

This research involved the development of a natural language processing (NLP)
architecture for the extraction of entity relation diagrams (ERDs) from natural language …