Permutations as a means to encode order in word space
作者： Magnus Sahlgren , Pentti Kanerva , Anders Holst
DOI:
关键词: Representation (mathematics) 、 Sequence 、 Coordinate vector 、 Word (computer architecture) 、 Convolution (computer science) 、 Natural language processing 、 Random indexing 、 Artificial intelligence 、 Word order 、 Mathematics 、 Sentence
摘要: We show that sequence information can be encoded into high-dimensional fixed-width vectors using permutations of coordinates. Computational models language often represent words with semantic compiled from word-use statistics. A word's vector usually encodes the contexts in which word appears a large body text but ignores order. However, order signals grammatical role sentence and thus tells meaning. Jones Mewhort (2007) included holographic reduced representation convolution. here captured also by permuting coordinates, providing general computationally light alternative to
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