While listening to Ruslan Mitkov presentation: Coreference Resolution: to What Extent Does it Help NLP Applications?, the thought occurred to me that coreference resolution lies at the core of topic maps.
A topic map can:
- Capture a coreference resolution in one representative by merging it with another representative that “pick out the same referent.”
- Define a coreference resolution by defining representatives that “pick out the same referent.”
- Interchange coreference resolutions by defining the representation of referents that “pick out the same referent.”
Not to denigrate associations or occurrences, but they depend upon the presence topics, that is representatives that “pick out a referent.”
Merged topics being two or more topics that individually “picked out the same referent,” perhaps using different means of identification.
Rather than starting every coreference resolution application at zero, to test its algorithmic prowess, a topic map could easily prime the pump as it were with known coreference resolutions.
Enabling coreference resolution systems to accumulate resolutions, much as human users do.*
*This may be useful because coreference resolution is a recognized area of research in computational linguistics, unlike topic maps.