Mining Unstructured Knowledge Bases for Question & Answers Websites - Lima, Eduardo
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Knowledge retrieval is a long standing problem that faces the challenge of dealing with natural language data. Such data is produced and consumed by humans and therefore is ambiguous and unstructured by nature. Recent studies focused on using unstructured data as sources for answers, with interesting results. We used Stack Overflow public data and searched for answers in external sources. Results showed that this approach is feasible. Furthermore, we explored machine-learned models and were able to improve the baseline results by leveraging the tag information predicted from new questions.

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Produktbeschreibung
Knowledge retrieval is a long standing problem that faces the challenge of dealing with natural language data. Such data is produced and consumed by humans and therefore is ambiguous and unstructured by nature. Recent studies focused on using unstructured data as sources for answers, with interesting results. We used Stack Overflow public data and searched for answers in external sources. Results showed that this approach is feasible. Furthermore, we explored machine-learned models and were able to improve the baseline results by leveraging the tag information predicted from new questions.
Autorenporträt
Eduardo Lima graduated as B.S. in Computer Engineering by UFRN, RN, Brazil in 2003. Since 2003, had lectured in IT Vocational Schools in Natal, Brazil. In 2013, started to teach in IFRN, RN, Brazil. In 2015, founded the RIT Data Science Group. In 2016, was granted the title of M.S. in Information Sciences by RIT, NY, US.