Córdoba-Rodas, Angie P.

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Semantic metadata extraction from open domain texts in natural language
(2013) Córdoba-Rodas, Angie P.; Vega-Riveros, José F.; College of Engineering; Rivera-Gallego, Wilson; Rodríguez-Martínez, Manuel; Department of Electrical and Computer Engineering; Carroll, Kevin S.
The information existing on the Web is growing immensely, and has posed a great challenge to users when searching for information and documents about a specific topic. Current search engines, though quite effective, fall short in many occasions in the relevance and accuracy of their results. Natural Language Processing (NLP) is a natural step towards understanding the searcher’s intent and the meaning of terms in context. In this research, a supervised learning algorithm was built to extract se- mantic metadata of the sentences from documents written in natural language. The training set for the system was a corpus which was built with semantic annotations of sentences from a paper on a specific subject. The semantic metadata describe the constituents of a sentence in terms of thematic roles. The constituents were obtained from the grammatical structure of the sentence using the Stanford University Natural Language Parser.