I have a list of words and phrases (~3k items). What are my options to extract them from documents (~3M of job descriptions) with NLP? I do not have labeled data.
For example my list of words and phrases look like,
Leadership Microsoft Office AWS . . . Python Programing Language
The result I am looking for is a matrix(3K x 3M) with binary values inside.
|Doc #||Leadership||Microsoft Office||AWS||...||Python Programing Language|
Regex - This is the most straightforward solution comes my mind. However, this solutions is not robust and cannot capture different word/phrase forms. For example, people might write
MS Officeinstead of
Microsoft Office. Similarly, people might write
Amazon Web Servicerather than
Is there a solution to utilize a Large Language Model such as BERT?
If I create a labeled data using, for example, AWS Ground Truth, is there a way to utilize the results to build a model and extract the list of words/phrases?