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How does this help with words, for example substantives, that can have completely different meanings depending on the context? Ex: The ball is bouncing on the
by Udik 11y ago
How does this help with words, for example substantives, that can have completely different meanings depending on the context? Ex:
The ball is bouncing on the floor
The ball takes place in the hotel
- nl 11y agoNot sure this will help much here, because in both of those cases "ball" is a noun. However, plain "old" word2vec will handle it fine. A round ball is associated with words like bounce, and various sporting terms. It will appear in a the vector space alongside those words. A dance ball is associated with dance, gowns, "go to", etc. The example in the paper around "Washington" shows how this can be used to distinguish between the NER (named entity resolution) type "PERSON" and "GPE" (Geo-political-entity) - it disambiguates very well between Washington (George) and Washington (DC).
- Udik 11y agoHmm, but isn't word2vec a dictionary in word -> vector? If it's the case, the two - completely unrelated - meanings of the word "ball" will be represented by a single vector. Which would mean that in that representation, for example, the concept of a ball is near both to "sphere" and "music" or "dancing". So that the perfect ball is a dancing sphere, or something like that.