Classifier subset selection for biomedical named entity recognition

Abstract

Classifier ensembling approach is considered for biomedical named entity recognition task. A vote-based classifier selection scheme having an intermediate level of search complexity between static classifier selection and real-valued and class-dependent weighting approaches is developed. Assuming that the reliability of the predictions of each classifier differs among classes, the proposed approach is based on selection of the classifiers by taking into account their individual votes. A wide set of classifiers, each based on a different set of features and modeling parameter setting are generated for this purpose. A genetic algorithm is developed so as to label the predictions of these classifiers as reliable or not. During testing, the votes that are labeled as being reliable are combined using weighted majority voting. The classifier ensemble formed by the proposed scheme surpasses the full object F-score of the best individual classifier by 2.75% and it is the highest score achieved on the data set considered.

A novel feature selection method based on normalized mutual information

“…Methods using (8) are referred as Max-Dependency (MD) approaches. Regardless of the searching algorithm, MD faces difficulties in estimating the multivariate density functions, which requires not only a high computational cost but also a large number of samples.…”

“…Therefore, removing irrelevant features helps speed up the learning process and alleviates the effect of the curse of dimensionality. Due to the capabilities, feature selection has been largely applied in many applications, including text classification [6,12], bio-informatics [8,24,32], intrusion detection [18,27], and image retrieval [5,9]. Furthermore, feature selection facilitates the data visualization and understanding [14,17,31].…”

Automatic extraction of acronym definitions from the Web

“…The recall value is identical in most of the cases but, when last quartile contains valid definitions, the value is lower. Considering the reduced amount of definitions available in Acronym Finder (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20), this fact affects significantly the final performance (lower FMeasure). Even though, in most cases, results are slightly better due to the improvement in selection accuracy.…”

“…New acronyms are defined every day for almost every possible domain of knowledge. This is especially evident in domains such as biomedicine [15,39]. • They are highly polysemic.…”

Mining the Online Social Network Data: Influence, Summarization, and Organization

“…A huge amount of available online textual documents in the field of biomedicine leads to great difficulties for building question answering, or information retrieval systems [DVA09]. Luckily, multi-document summarization can assist extracting the essential information from those documents and hereby benefit those systems.…”