Abacha, Asma Ben and Garcia Seco De Herrera, Alba and Wang, Ke and Long, L Rodney and Antani, Sameer and Demner-Fushman, Dina (2017) Named Entity Recognition in Functional Neuroimaging Literature. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017-11-13 - 2017-11-16, Kansas City, MO, USA.
Abacha, Asma Ben and Garcia Seco De Herrera, Alba and Wang, Ke and Long, L Rodney and Antani, Sameer and Demner-Fushman, Dina (2017) Named Entity Recognition in Functional Neuroimaging Literature. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017-11-13 - 2017-11-16, Kansas City, MO, USA.
Abacha, Asma Ben and Garcia Seco De Herrera, Alba and Wang, Ke and Long, L Rodney and Antani, Sameer and Demner-Fushman, Dina (2017) Named Entity Recognition in Functional Neuroimaging Literature. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017-11-13 - 2017-11-16, Kansas City, MO, USA.
Abstract
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 rulebased 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.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Published proceedings: _not provided_ |
Uncontrolled Keywords: | conditional random fields, Functional Magnetic Resonance Imaging, machine learning, manual annotation, Named Entity Recognition, rule-based methods |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 03 Dec 2018 16:45 |
Last Modified: | 30 Oct 2024 16:44 |
URI: | http://repository.essex.ac.uk/id/eprint/22213 |
Available files
Filename: bibm1017poster3pages.pdf