By Henning Wachsmuth
This monograph proposes a finished and completely computerized method of designing textual content research pipelines for arbitrary details wishes which are optimum when it comes to run-time potency and that robustly mine suitable info from textual content of any variety. according to state of the art suggestions from laptop studying and different parts of synthetic intelligence, novel pipeline development and execution algorithms are constructed and applied in prototypical software program. Formal analyses of the algorithms and large empirical experiments underline that the proposed technique represents an important step in the direction of the ad-hoc use of textual content mining in net seek and large facts analytics.
Both net seek and large information analytics goal to satisfy peoples’ wishes for info in an adhoc demeanour. the knowledge searched for is usually hidden in quite a lot of usual language textual content. rather than easily returning hyperlinks to most likely appropriate texts, best seek and analytics engines have began to without delay mine proper details from the texts. To this finish, they execute textual content research pipelines that could include numerous advanced information-extraction and text-classification phases. as a result of useful necessities of potency and robustness, notwithstanding, using textual content mining has up to now been restricted to expected info wishes that may be fulfilled with really uncomplicated, manually developed pipelines.
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Text Analysis Pipelines: Towards Ad-hoc Large-Scale Text Mining (Lecture Notes in Computer Science) by Henning Wachsmuth
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