@edobobo Hi, unrelated to my previous issue :) You seem to be an interesting researcher in NLP/NLU so I figured I could invite you in my pantheon.
I believe I have useful knowledge to share to you but before, I have a problem and maybe you know a solution:
I work on semantic parsing and my current task is on finding algorithmic steps to resolve the understanding and disambiguation of prepositions.
E.g.
Bob is in the kitchen.
Bob is in love.
Here the preposition in play two different role, one refer to a location (the kitchen) and the other refer to a state (love)
I first tought that solving wether a "in" is about a spatial location would be easy by using a NER neural network. Unfortunately I came to realize that Named Entity Recognition only works for named things, AKA proper nouns.
However the kitchen is a common noun. I am therefore looking for neural networks that can classify common nouns as being a spatial (or temporal) location and I just can't find one... I don't know how this tasks even is named... (could be called anonymous/unnamed entity recognition although it would also refer to e.g "The orange man" being an anonymous and non-serious way to refer to Trump (and Trump is a partial way to refer to Donald.Jr.Trump)
There are workarounds, by not solving the problem directly (e.g. one can use a generic WSD NN or a specialized WSD NN for classifying prepositions supersenses) or one can check the wordnet hypernyms of a word (e.g) kitchen and stops at "area" or "physical entity" to ascribe to it the meaning of a spatial location. But is that really the case? Is the detection of spatial and temporal words a totally neglected task in NLP or is there something similar to NER that I never heard of ?
@edobobo Hi, unrelated to my previous issue :) You seem to be an interesting researcher in NLP/NLU so I figured I could invite you in my pantheon. I believe I have useful knowledge to share to you but before, I have a problem and maybe you know a solution:
I work on semantic parsing and my current task is on finding algorithmic steps to resolve the understanding and disambiguation of prepositions. E.g. Bob is in the kitchen. Bob is in love.
Here the preposition in play two different role, one refer to a location (the kitchen) and the other refer to a state (love) I first tought that solving wether a "in" is about a spatial location would be easy by using a NER neural network. Unfortunately I came to realize that Named Entity Recognition only works for named things, AKA proper nouns. However the kitchen is a common noun. I am therefore looking for neural networks that can classify common nouns as being a spatial (or temporal) location and I just can't find one... I don't know how this tasks even is named... (could be called anonymous/unnamed entity recognition although it would also refer to e.g "The orange man" being an anonymous and non-serious way to refer to Trump (and Trump is a partial way to refer to Donald.Jr.Trump) There are workarounds, by not solving the problem directly (e.g. one can use a generic WSD NN or a specialized WSD NN for classifying prepositions supersenses) or one can check the wordnet hypernyms of a word (e.g) kitchen and stops at "area" or "physical entity" to ascribe to it the meaning of a spatial location. But is that really the case? Is the detection of spatial and temporal words a totally neglected task in NLP or is there something similar to NER that I never heard of ?