Does NER work on large documents around 1500 - 3000 words or so? Let's say I have a resume and I have segmented the work section.
Usually work section of resume contains company name, designation, work period and job description. Now I have 1000's of resumes and I have manually annotated the work section of each resume with those 4 labels.
But the problem here is each work section is quite large around 1400- 3000 words?
Also job-description annotation is not one word like company name etc...  the entire responsibilities of the job is annotated as one entity.
So, will this work? please let me know what are the things I should consider for such NER entity extraction?
Any suggestions would really help. Thank you in advance.
The size of the document on which NER is run shouldn't be a problem at all, a standard NER system scans the document sequentially and just marks any entity it finds.
The size of the entities to find might be more of an issue, because typical NER systems rely on the previous few words to detect the boundaries of an entity. If the entity spans a large sequence of text, it's harder for the system to detect where it ends. In case it actually causes a problem with your data, it might be possible to specify exactly which features to use in the CRF model (this depends on the implementation I guess).