NLP Methodology as Guidance and Verification of the Data Mining of Survey ENSANUT 2012

Abstract

Data Mining represents the cutting edge when we think about extracting information; however it always implicates a considerable spent provided that it needs “structured data”. Following this idea, text mining appears in the horizon, as a little spent, reliable alternative. It is able to provide meaningful expert information without the availability of plenty of resources, all we need is a fair big (real big) corpus of text in order to conduct a research on almost every topic. By themselves, both approaches provide valuable information at the end, nevertheless what would happen if both processes were linked in a way that one approach’s results could be verify by the result of a second process? With this idea on mind we are relaying on one hypothesis this is possible to generate a bound between both mining process and using them back and forth to verify one another. Hence, we describe thoroughly both methodologies making a special emphasis on mentioning those phases which have a propensity to establish a strong bound between them. We found that bound in the fact that once a Natural Language Processing has been performed on the chosen corpora what we got as an output is a list of meaningful nouns which can be used as features that will guide in a reliable way a data mining process.