Title: BNEMiner: mining biomedical literature for extraction of biological target, disease and chemical entities

Authors: Sindhuja Gopalan; Sobha Lalitha Devi

Addresses: AU-KBC Research Centre, MIT Campus, Anna University, Chennai, India ' AU-KBC Research Centre, MIT Campus, Anna University, Chennai, India

Abstract: The paper presents a novel application to extract biomedical entities automatically using machine learning techniques from large volumes of biomedical text. The data in large quantities are accumulating day by day and requires automatic extraction of information. Data mining is the science of extracting information from large data. Biomedical Named entity recognition (BioNER) is the task of data mining that extracts named entities from biological texts. In this paper, we focus on developing a BioNER system for extraction of biological target, disease and chemical entities from biomedical texts. We developed the system using graphical based machine learning technique the CRFs. We have applied a set of diverse features containing standard lexical, syntactic and orthographic features combined with novel and biologically inspired features, action terms and process verbs. The system was evaluated with three widely recognised datasets. The results demonstrated the portability and the potency of the system.

Keywords: data mining; biomedical entities; graph-based modelling; biologically motivated features; portability; biomedical literature; entity extraction; biological target entities; disease entities; chemical entities; machine learning; named entities; biological texts.

DOI: 10.1504/IJBIDM.2016.081612

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.2, pp.190 - 204

Received: 01 Sep 2016
Accepted: 05 Oct 2016
Published online: 17 Jan 2017 *

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