> - Establishing relationships between entities in a body of text. E.g. we could build a fact-graph from sentences like "Burning coal increases CO2", and "CO2 increase induces global warming". Useful also in medical literature where there are millions of pathways.
That's a simple example because with 'CO2' you at least have the same string that can serve as a keyword connecting those two facts. Usually in natural language we make frequent use of anaphora to refer to people, objects and concepts previously mentioned in the text by name.
Anaphora resolution is one of the really hard problems not only in NLP but in linguistics in general. The most simple anaphoric device in languages like English is pronouns and even with those it can be quite difficult to determine what a 'he' or 'she' refers to in context.
>Anaphora resolution is one of the really hard problems not only in NLP but in linguistics in general.
This was one of the most frustrating parts of studying Latin rhetoric. The speakers would keep referring to "That thing I was talking about," and it's a noun from a subordinate clause 2 and a half paragraphs ago.
That’s actually very common in most languages. English is one of the few western languages that doesn’t do this, which makes it quite complicated for some people to write sentences in it, as in their native language such far backreferences, and long run-on sentences may be a lot more common.
That's a simple example because with 'CO2' you at least have the same string that can serve as a keyword connecting those two facts. Usually in natural language we make frequent use of anaphora to refer to people, objects and concepts previously mentioned in the text by name.
Anaphora resolution is one of the really hard problems not only in NLP but in linguistics in general. The most simple anaphoric device in languages like English is pronouns and even with those it can be quite difficult to determine what a 'he' or 'she' refers to in context.