PTN-07 Benchmarking Of A Novel POS Tagging Based Semantic Similarity Approach For Job Description Similarity Computation

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Most solutions providing hiring analytics involve mapping provided job descriptions
to a standard job framework, thereby requiring computation of a document similarity score
between two job descriptions. Finding semantic similarity between a pair of documents is a
problem that is yet to be solved satisfactorily over all possible domains/contexts. Most
document similarity calculation exercises require a large corpus of data for training the
underlying models. In this paper we compare three methods of document similarity for job
descriptions - topic modeling (LDA), doc2vec, and a novel part-of-speech tagging based
document similarity (POSDC) calculation method. LDA and doc2vec require a large corpus of
data to train, while POSDC exploits a domain specific property of descriptive documents
(such as job descriptions) that enables us to compare two documents in isolation. POSDC
method is based on an action-object-attribute representation of documents, that allows
meaningful comparisons. We use stanford Core NLP and NLTK Wordnet to do a multilevel
semantic match between the actions and corresponding objects. We use sklearn for topic
modeling and gensim for doc2vec. We compare the results from these three methods based
on IBM Kenexa Talent frameworks job taxonomy.