#Skill Set Mapping to Jobs

Step 1. Candidate Feature Set

We looked at N-grams in the range [2,4] that starts with trigger words such as 'perform','deliver', ''ability', 'avail' 'experience','demonstrate' or contain words such as knowledge', 'licen', 'educat', 'able', 'cert' etc. and harvested a large set of n-grams.

See: candidate_features.tsv

Step 2. Coarse clustering

We performed a coarse clustering using KNN on stemmed N-grams, and generated 20 clusters.

They roughly clustered around the following hand-labeled themes. At this stage we found some interesting clusters such as disabled veterans & minorities.

Cluster	Number of Features
truly unknown	7971
knowledge	5199
unknown	4050
education	2944
previous experience	2931
skills	1844
unknown/able	1807
unknown/ability	1266
license	1070
accountable and accounts	404
delivery	360
communication	343
required years of experience	313
disabled veterans & minorities	311
sales experience	305
education experience	297
drivers license	281
customer service	262
tables/microsoft/cleaning	218
computer skills	169
dependable	161
merchandise	60

Big clusters such as Skills, Knowledge, Education required further granular clustering.

Step 3. Reclustering using semantic mapping of keywords

Within the big clusters, we performed further re-clustering and mapping of semantically related words.

For this, we used python-nltks wordnet.synset feature. With this semantically related key phrases such as 'arithmetic skills', 'basic math', 'mathematical ability' could be mapped to a single cluster.

The end result of this process is a mapping of a skill tag to several feature words that can be matched in the job description text.

For example:

skill tag	feature words
Analytic and Mathematical Ability	math, mathematics, arithmetic, analytic, analytical, ...

Step 4. Matching Skill Tag to Job description

At this step, for each skill tag we build a tiny vectorizer on its feature words, and apply the same vectorizer on the job description and compute the dot product. A value greater than zero of the dot product indicates at least one of the feature words is present in the job description. If so, we associate this skill tag with the job description.

Step 5: Convert the operation in Step 4 to an API call.

Under api/ we built an API that given a Job ID will return matched skills.

##Usage:

To fire up the API, run

python server.py

Tests

Under unittests/ run python test_server.py

##Dependencies

• A job description call: The API makes a call with the job_id to get the job description text. This could be a database call, but in the method=local context, it read it off the local file /model/job_desc.json
• Skill to feature map: model/skillname_to_skilltags.tsv
  • Each line in file has the format skill name:keyword_1,keyword_2,....,keyword_n. e.g. sales skills:sale,sales,selling,retail
• Stop words: / model / skill_stop_words.txt provides a way to ignore or blacklist words in the vectorization steps.

Call and Response:

The API is called with a json payload of the format: {"job_id": "10000038"}

If the job id/description is not found, the API returns an error ERROR: job text could not be retrieved

If the job description could be retrieved and skills could be matched, it returns a response like:

  {"0": {"skill": "interpersonal and communication skills", "score": 2, "matched_tags": ["communication", "interpersonal"]}, "1": {"skill": "sales skills", "score": 4, "matched_tags": ["selling", "retail", "sale", "sales"]}}

Here, two skills could be matched to the job, namely "interpersonal and communication skills" and "sales skills". It also shows which keywords matched the description and a score (number of matched keywords) for father introspection.