Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry
| Autor: | Lezan Hawizy, BalaKrishna Kolluru, Peter Murray-Rust, Jun'ichi Tsujii, Sophia Ananiadou |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
PubMed
Text Mining Interoperability lcsh:Medicine Biology computer.software_genre Bioinformatics Markov model 01 natural sciences Computer Applications Workflow 03 medical and health sciences Computational Chemistry Named-entity recognition Terminology as Topic lcsh:Science 030304 developmental biology Graphical user interface Natural Language Processing 0303 health sciences Multidisciplinary Markov chain Chemistry business.industry Maximum-entropy Markov model lcsh:R Computational Biology Reproducibility of Results Computing Methods Markov Chains 0104 chemical sciences 010404 medicinal & biomolecular chemistry Computer Science lcsh:Q Artificial intelligence business computer Classifier (UML) Natural language processing Algorithms Research Article |
| Zdroj: | PLoS ONE PLoS ONE, Vol 6, Iss 5, p e20181 (2011) |
| ISSN: | 1932-6203 |
| Popis: | Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. |
| Databáze: | OpenAIRE |
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