Automated analysis of battery articles
Abstract: Journal articles are the formal medium for the communication of results among scientists, and often contain valuable data. However, manually collecting article data from a large field like lithium-ion battery chemistry is tedious and time consuming, which is an obstacle when searching for statistical trends and correlations to inform research decisions. To address this a platform for the automatic retrieval and analysis of large numbers of articles is created and applied to the field of lithium-ion battery chemistry. Example data produced by the platform is presented and evaluated and sources of error limiting this type of platform are identified, with problems related to text extraction and pattern matching being especially significant. Some solutions to these problems are presented and potential future improvements are proposed.
AT THIS PAGE YOU CAN DOWNLOAD THE WHOLE ESSAY. (follow the link to the next page)