Representation of Relations by Planes in Neural Network Language Model
Whole brain architecture (WBA) which uses neural networks to imitate a human brain is attracting increased attention as a promising way to achieve artificial general intelligence, and distributed vector representations of words is becoming recognized as the best way to connect neural networks and knowledge. Distributed representations of words have played a wide range of roles in natural language processing, and they have become increasingly important because of their ability to capture a large amount of syntactic and lexical meanings or relationships. Relation vectors are used to represent relations between words, but this approach has some problems; some relations cannot be easily defined, for example, sibling relations, parent-child relations, and many-to-one relations. To deal with these problems, we have created a novel way of representing relations: we represent relations by planes instead of by vectors, and this increases by more than 10 % the accuracy of predicting the relation.
KeywordsAccuracy Rate Representation Space Relation Vector Euclid Distance Function Sibling Relation
This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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