Extracting Information for Meaningful Function Inference through Text-Mining

Pan, Hong; Zuo, Li; Kanagasabai, Rajaraman; Zhang, Zhuo; Choudhary, Vidhu; Mohanty, Bijayalaxmi; Tan, Sin Lam; Krishnan, S. P. T.; Veladandi, Pardha Sarathi; Meka, Archana; Choy, Weng Keong; Swarup, Sanjay; Bajić, Vladimir B.

doi:10.1007/0-387-36747-0_5

Cited by 2 publications

(1 citation statement)

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“…A NER tool can then be trained using these examples via one of the standard libraries, such as CRF++, 1 or NERSuite. 2 The NER tool can then be applied to further documents, or document collections, as a building block for more complex tasks such as curation (Ambert et al 2013 ), information retrieval (Müller et al 2008 ) and inference (Pan et al 2006 ). Notable efforts to create named entity recognition tools for neuroscience have focussed on the identification of brain regions (French et al 2009 ; French et al 2012 ), neuron types (Ambert et al 2013 ), brain connectivity (Vasques et al 2015 ; Richardet et al 2015a ), and a number of entities related to spinal cord injuries (Stöckel et al 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

Shardlow

et al. 2018

Neuroinform

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The curation of neuroscience entities is crucial to ongoing efforts in neuroinformatics and computational neuroscience, such as those being deployed in the context of continuing large-scale brain modelling projects. However, manually sifting through thousands of articles for new information about modelled entities is a painstaking and low-reward task. Text mining can be used to help a curator extract relevant information from this literature in a systematic way. We propose the application of text mining methods for the neuroscience literature. Specifically, two computational neuroscientists annotated a corpus of entities pertinent to neuroscience using active learning techniques to enable swift, targeted annotation. We then trained machine learning models to recognise the entities that have been identified. The entities covered are Neuron Types, Brain Regions, Experimental Values, Units, Ion Currents, Channels, and Conductances and Model organisms. We tested a traditional rule-based approach, a conditional random field and a model using deep learning named entity recognition, finding that the deep learning model was superior. Our final results show that we can detect a range of named entities of interest to the neuroscientist with a macro average precision, recall and F1 score of 0.866, 0.817 and 0.837 respectively. The contributions of this work are as follows: 1) We provide a set of Named Entity Recognition (NER) tools that are capable of detecting neuroscience entities with performance above or similar to prior work. 2) We propose a methodology for training NER tools for neuroscience that requires very little training data to get strong performance. This can be adapted for any sub-domain within neuroscience. 3) We provide a small corpus with annotations for multiple entity types, as well as annotation guidelines to help others reproduce our experiments. Electronic supplementary material The online version of this article (10.1007/s12021-018-9404-y) contains supplementary material, which is available to authorized users.

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