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I have a large data set with over a million products. The NLP results look like this: this

A random row (reshaped) looks like this: this

The dataframe (image) contains information derived from the tokenization of a single product (" Dipperwell Faucet with Drain Stainless Steel Bowl Inner Overflow Cup").

Is it possible to predict the word "faucet" given the NLP output. The ML algorithms I am familiar with usually are in a format

Prediction~ Predictor 1+ Predictor 2.. etc..

In this case , the predictors are columns and the predicted value is a cell

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  • $\begingroup$ "Is it possible"? I suppose so. Can you make your question more specific? $\endgroup$ – gung Jun 8 '16 at 3:49
  • $\begingroup$ How do you recommend I do it? I will have to manually create a training set where the predicted variable is a token/ a combination of tokens. $\endgroup$ – Piyush Saxena Jun 10 '16 at 13:31
  • $\begingroup$ What is the NLP output? $\endgroup$ – Franck Dernoncourt Jun 26 '16 at 19:30
  • $\begingroup$ The output is tokens, POS and relationship between tokens . Thus, all categorical variables $\endgroup$ – Piyush Saxena Jun 28 '16 at 13:59
  • $\begingroup$ don't post images of data. hard to copy and paste and read $\endgroup$ – John H Oct 8 '18 at 2:19
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I think you're trying to tag/classify your products into product "buckets" like faucets?

If so, the features you've proposed are ill-suited to a statistical approach. You should investigate word vectors as features. https://cs.stanford.edu/~quocle/paragraph_vector.pdf

You will still need to annotate a dataset, or provide "faucet" for each of some set observations for training. You could use the NLP techniques you're already employing to automate a portion of that work, effectively giving you a "suggestion" for you to review and edit if necessary.

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