An information extraction tool for microbial characters

Mao, Jin; Moore, Lisa; Blank, Carrine; Cui, Hong

Permalink

https://hdl.handle.net/2142/89385 Copy

Description

Title

An information extraction tool for microbial characters

Author(s)

Mao, Jin; Moore, Lisa; Blank, Carrine; Cui, Hong

Issue Date

2016-03-15

Keyword(s)

• Phenotypic Character Extraction
• Information Extraction
• Natural Language Processing
• Text Classification

Abstract

Automated extraction of phenotypic and metabolic characters from microbial taxonomic descriptions will benefit biology research and study. In this poster, we describe a Microbial Phenomics Information Extractor (MicroPIE) system. MicroPIE takes taxonomic descriptions in XML files as input and can extract 58 types of microbial characters. The main extraction steps are :1) splitting paragraphs into sentences; 2)predicting the characters described in the sentences by using automated classifiers; 3)extracting character values from the sentences by applying a variety of methods, such as Regular Expression Rule, Term Matching, and Unsupervised Semantic Parsing. Parts of the system have been implemented and currently been optimized for better performance. Results on optimizing the sentence classifiers show that the SVMs (Support Vector Machines) achieved better performance over the Naive Bayes classifiers, in addition, resolving the problem of unbalanced training instances helped improve the performance of SVMs.

Publisher

iSchools

Series/Report Name or Number

IConference 2016 Proceedings

Type of Resource

text

Language

eng

Permalink

http://hdl.handle.net/2142/89385

DOI

https://doi.org/10.9776/16562

Copyright and License Information

Copyright 2016 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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