Linking Stanford Typed Dependencies to Support Text Analytics
With the daily increase of the amount of published information, research in the area of text analytics is gaining more visibility. Text processing for improving analytics is being studied from different angles. In the literature, text dependencies have been employed to perform various tasks. This includes for example the identification of semantic relations and sentiment analysis. We observe that while text dependencies can boost text analytics, managing and preserving such dependencies in text documents that spread across various corpora and contexts is a challenging task. We present in this paper our work on linking text dependencies using the Resource Description Framework (RDF) specification, following the Stanford typed dependencies representation. We contribute to the field by providing analysts the means to query, extract, and reuse text dependencies for analytical purposes. We highlight how this additional layer can be used in the context of feedback analysis by applying a selection of queries passed to a triple-store containing the generated text dependencies graphs.