This program given a piece of text such as a cv, job summary or a Linkdein profile converts it to a 300d vector (using average of word vectors) and ranks ONET job titles based on similarity to that description. The ONET is a standard dataset consisting of about 1100 job titles and their description. It includes other information about jobs that we didn't use here.

For each job title and description, a 300d average word vector is built. Given a piece of text the program finds the most similar job titles related to that text.

The similarity/distance distribution of a piece of text to a 1100d job titles can be used for comparison to another piece of text to see if both pieces of text are corresponding to one person or not using cosine distance between them.

If two pieces of text correspond to the same person their distance to 1100 job titles should be similar (their cosine distance should be low).

The cosine distance between two pieces of text can be used as a single feature when trying to decide if two pieces of text correspond to a single person or not.

To run the program gensim should be installed and the pre-trained Google word2vec file should be downloaded and the path in the source changed accordingly.

download it from https://docs.google.com/uc?id=0B7XkCwpI5KDYNlNUTTlSS21pQmM&export=download and move it into the resources directory.

can be downloaded from ONET dataset here https://www.onetcenter.org/dl_files/database/db_21_0_text/Occupation%20Data.txt.

Afshin Rahimi [email protected]