Textkernel
Daniel Foster is a Research Engineer at Textkernel, specializing in natural language processing, information extraction, and automated candidate/job matching systems since April 2018. Responsibilities include implementing quality improvements, software development in Python and Perl, and data science techniques using Jupyter notebooks. Prior experience includes a role as a Search Quality Analyst at Textkernel, conducting analysis of search relevance and assisting in code updates. Daniel’s background also encompasses a Research Assistant Internship at Meertens Instituut, focusing on language studies, and a position as an Analyst/Senior Analyst at Overseas Strategic Consulting, managing communications for USAID projects in Afghanistan. Daniel holds Master's degrees in Language Technology and Linguistics from the University of Gothenburg and the University of Amsterdam, respectively, as well as a Bachelor's degree in Spanish and International Studies from Virginia Tech.
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Textkernel
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Textkernel translates text mining and information extraction into effective business solutions.Textkernel is developing technology for advanced information extraction and text understanding, in order to provide computers with full power over large volumes of unstructured textual information. Textkernel's free format text recognitiontechnology is packaged into Textractor Enterprise, an adaptive multi-lingual Information Extraction engine. Textractor is easily trained on domain specific labeled examples to capture key semantic information from your documents, regardless of their language, format, layout or vocabulary. The result is an intelligent and high accuracy conversion of unstructured text into easy-to-integrate structured output. Textkernel delivers fully engineered end-to-end solutions that solve complex data entry automation tasks in a variety of application contexts.