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 |
---|---|---|---|---|---|
1 | 1 | 0 | 0 | 1 | |
. | . | . | . | . | |
3M | 0 | 1 | 1 | 1 |
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 Office
instead ofMicrosoft Office
. Similarly, people might writeAmazon Web Service
rather thanAWS
.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?