I am working on a project that involves picking out specific kinds of objects from text. The documents I am going though are life sciences and biomedical in nature, and in these documents there are specific biomedical "objects" I want to pick out. The nature and variety of the text objects means I can't use regex or string matching. It has to be some kind of classification.
These text objects can be one word, or multiple words, but they are always in sequence. An example sentence would be like
During the process of protein synthesis, X was used.
I need to pick out X. Luckily, I have plenty of labeled documents, and plenty of labels to go along with it. So I know a human can pick out these objects. So the challenge now is to get a machine to be able to pick out these types of objects from unseen text. I am working under the assumption that these specific text objects all fall under somewhat similar grammatical and textual context, so given enough labeled data, a machine should be able to learn how to pick out the text object.
Two Main Questions.
How do I label specific words in a document such that some model will understand that given a sequence of text, the object at position Y is a labeled and what we should be trying to classify.
Does anything I just said make any sense? Is there any research on what I've been talking about, because I've looked around and have not been able to find much.