Linguistic Analysis and Data Extraction with IBM Watson Content Analytics
January 30, 2015
The article on IBM titled Discover and Use Real-World Terminology with IBM Watson Content Analytics provides an overview to domain-specific terminology through the linguistic facets of Watson Content Analytics. The article begins with a brief reminder that most data, whether in the form of images or texts, is unstructured. IBM’s linguistic analysis focuses on extracting relevant unstructured data from texts in order to make it more useful and usable in analysis. The article details the processes of IBM Watson Content Analytics,
“WCA processes raw text from the content sources through a pipeline of operations that is conformant with the UIMA standard. UIMA (Unstructured Information Management Architecture) is a software architecture that is aimed at the development and deployment of resources for the analysis of unstructured information. WCA pipelines include stages such as detection of source language, lexical analysis, entity extraction… Custom concept extraction is performed by annotators, which identify pieces of information that are expressed as segments of text.”
The main uses of WCA are exploring insights through facets as well as extracting concepts in order to apply WCA analytics. The latter might include excavating lab analysis reports to populate patient records, for example. If any of these functionalities sound familiar, it might not surprise you that IBM bough iPhrase, and much of this article is reminiscent of iPhrase functionality from about 15 years ago.
Chelsea Kerwin, January 30, 2014
Sponsored by ArnoldIT.com, developer of Augmentext
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