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

Classifier ensembling approach is considered for biomedical named entity recognition task.
A vote-based classifier selection scheme having an intermediate level of search complexity …

We propose a genetic algorithm for constructing a classifier ensemble using a vote-based
classifier selection approach for biomedical named entity recognition task. Assuming that the …

In named entity recognition (NER) for biomedical literature, approaches based on combined
classifiers have demonstrated great performance improvement compared to a single (best) …

A drug-drug interaction occurs when one drug influences the level or activity of another drug.
The detection of drug interactions is an important research area in patient safety since these …

This paper presents LIMSI's participation in the User-Centered Health Information Retrieval
task (task 2) at the CLEF eHealth 2015 workshop. In our contribution we explored two …

Abstract Named Entities (NEs) in biomedical text refer to objects that are of interest to
biomedical researchers, such as proteins and genes. Accurately identifying them is …

Over the last few years, a growing number of techniques has been successfully proposed to
tackle diverse challenges in the biomedical text mining (BioTM) arena. However, the set of …

This paper details the SysBio Explorer, a Systems Biology Literature Retrieval and
Processing Framework, whose aim relies on the automatic inference of regulatory and …