Suitable methodology to assign job titles to categories I am trying to find an automated way to assign job titles to categories.
My input data consists of job titles that are already assigned to a category and I want to be able to assign new job titles to one of those categories that fits best according to the already categorized ones.
The titles are not standardized. So one title could be &quot;Marketing manager&quot; that is already assigned to category A and a new title that should be assigned could be &quot;Head of marketing&quot;. Therefore a simple 1:1 mapping of title to category will not be possible.
I appreciate any hints into the right direction.
EDIT:
The prerequisites have now changed so that I have specific job codes rather than varying job titles. So the dataset looks something like this:
category, jobCode, seniority, location
1, 1, CXO, us
1, 4, intern, us
2, 3, manager, uk
2, 4, intern, us

So the task is now to provide a function that uses the components jobCode, seniority and location to output the most suitable category based on the existing assignments.
I used word2vec to assign job titles to the jobCodes. Is this something I should expand on. E.g. just concatenate jobcode seniority and location and treat them as an input for word2veb?
I suppose there as a much more straight forward and suitable algorithm for this kind of problem.
This is regular text classification, but with very little text (only the job title). You could start with a simple one-hot encoding over the words in the job title, then apply your favourite algorithm (e.g. Naive Bayes, Decision Trees, etc). It will probably work better with some form of normalization of the words (at least using the lemma in order to mach variants of the same word).
Word embeddings are probably a good choice too, but my guess is that this would help only if you have a quite high amount of examples.