2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2017)
Kansas City, MO, USA
Nov. 13, 2017 to Nov. 16, 2017
Asma Ben Abacha , National Library of Medicine, Bethesda, MD, USA
Alba G. Seco de Herrera , University of Essex, Colchester, UK
Ke Wang , Swarthmore College, Swarthmore, Pennsylvania, USA
L. Rodney Long , National Library of Medicine, Bethesda, MD, USA
Sameer Antani , National Library of Medicine, Bethesda, MD, USA
Dina Demner-Fushman , National Library of Medicine, Bethesda, MD, USA
Human neuroimaging research aims to find mappings between brain activity and broad cognitive states. In particular, Functional Magnetic Resonance Imaging (fMRI) allows collecting information about activity in the brain in a non-invasive way. In this paper, we tackle the task of linking brain activity information from fMRI data with named entities expressed in functional neuroimaging literature. For the automatic extraction of those links, we focus on Named Entity Recognition (NER) and compare different methods to recognize relevant entities from fMRI literature. We selected 15 entity categories to describe cognitive states, anatomical areas, stimuli and responses. To cope with the lack of relevant training data, we proposed rule-based methods relying on noun-phrase detection and filtering. We also developed machine learning methods based on Conditional Random Fields (CRF) with morpho-syntactic and semantic features. We constructed a gold standard corpus to evaluate these different NER methods. A comparison of the obtained F1 scores showed that the proposed approaches significantly outperform three state-of-the-art methods in open and specific domains with a best result of 78.79% F1 score in exact span evaluation and 98.40% F1 in inexact span evaluation.
Brain, Semantics, Libraries
A. Ben Abacha, A. G. de Herrera, K. Wang, L. R. Long, S. Antani and D. Demner-Fushman, "Named entity recognition in functional neuroimaging literature," 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, USA, 2017, pp. 2218-2220.