Difference Between Deep Learning And Semantic Technology

This post talks about two approaches of Natural Language Processing (NLP) technology that you may wish to review, understand, and take into consideration while selecting the best fit for your enterprise needs.

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From an academic proposition, the strength of deep learning (or old-fashioned neural network, SVM based machine learning, etc) is that, it is relatively low cost. To implement machine learning, as the name suggests, all you have to do is to “train” the machine with a bunch of data.
The downside aspect of this approach however, is the compromising of its quality. Especially in the case where there is an absence of large amount of data comparing to social media sites such as Twitter and Facebook, the shortage of training data will result in poor quality. 

Unlike deep learning approach, semantic approach (in other words ontologies with rules) are comparably high in cost, though with proper investment (in developing rules and ontologies), it can achieve superior quality.

To each its own, both approaches have their own merits and downsides depending on your enterprise needs. If the budget is limited, go with deep learning, if the quality is a critical success factor, go with semantic approach. If you want to build your own product or solution, I would recommend a combination of these methods (in other words, the hybrid tool). The hybrid approach can be the most flexible and stable method to deal with enterprise data though there may be the need for a more experienced architectural consideration and a higher maintenance cost.

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