The job description contains a wealth of information regarding the actual details of the job, including but not limited to the location, salary, job start date, and much more. The more we can extract from the description, the happier our users will be when they can easily see the job parameters and more easily find what they are looking for. Using string matching and Natural Language Processing (NLP) is the best way to extract information from a job.
TODO
[ ] In the backend/data_processing folder, add some modules and files (organized) that increase our data processing capabilities
Notes
Consider having one function that returns some kind of a dictionary with keys that correlate to information that is wanted from the job. For example, one key, value pair could return { "location": "Houston, TX" }.
Also, NLTK can extract location information from a body of text (docs, so look into that!)
Context
The job description contains a wealth of information regarding the actual details of the job, including but not limited to the location, salary, job start date, and much more. The more we can extract from the description, the happier our users will be when they can easily see the job parameters and more easily find what they are looking for. Using string matching and Natural Language Processing (NLP) is the best way to extract information from a job.
TODO
backend/data_processingfolder, add some modules and files (organized) that increase our data processing capabilitiesNotes
Consider having one function that returns some kind of a dictionary with keys that correlate to information that is wanted from the job. For example, one key, value pair could return
{ "location": "Houston, TX" }.Also, NLTK can extract location information from a body of text (docs, so look into that!)