How would NLP handle semantic searches for spatial information?
The way I would approach this is to look at the prepositions in the first place, in this case near means close by. You then identify the reference point (train station). Now you find cafes which are close to that, ie you should have a list of cafes with their coordinates, and you compare those against the coordinates of the train station, returning the ones that are closest.
Other prepositions (opposite) or other descriptions (in the same street as) would need corresponding other metrics to evaluate whether they fit.
This is not a semantic search problem, as there is nothing inherent in language that describes whether something is close or far from another thing -- you need to map this onto the 'world', and make a decision from non-linguistic data.
Given a spatial related search phrase, such as "Find cafes near the train station", what would be the approach to handling this with NLP / semantic searching?
In this case, would all the 'cafes' need to have a qualitative token with regard to their distance to the train station (e.g. near / far)? Curious to know what the thought process would be for handling these kind of tasks.
Thanks!