This article sketches an NLP approach to pricing natural language words or phrases. It leverages creatively (1) the model word2vec, which learns the context and associations between words from a given corpus; (2) the Mondovo dataset, which provides basic building blocks for us to further bootstrap our application. This solution will have interesting applications in fields such as online ad bidding, online marketing, search engine optimization, etc. This article serves as an illustration of an initial baseline solution to the pricing problem and readers eager to learn more about how I do it in practice and a more in-depth treatment of the topic are welcome to tune in for my followup publication.

People are quantifying everything. When we are unable to do that to something, we call it either worthless or mysterious, or dismiss it adroitly as hallucination; such is the case with things like love, loyalty, honesty etc.

The online ad bidding industry is definitely not an exception, and one of their biggest problem is how to come up with accurate bid prices for their chosen ad keywords or phrases to secure some hot ad spots on the publishers’ websites. The quandary goes like this: if the bid price is set too high, you may be sure to get the ad spot, but you will also have to pay the hefty price you bid at; if you set the bid price too low, chances are that you will have a hard time getting that ad spot at all. Apparently, this delicate trade-off here calls for creative solutions to the problem of quantifying words/phrases into prices.

Fortunately, we can rest assured of the resounding good news: **words can be priced too! **For this problem, we might not have the luxury of a well-crafted recipe like the Black-Scholes model for options pricing, but there are multiple ways by which we can take a crack at it.

#artificial-intelligence #advertising #programming #machine-learning #data-science #deep learning