Similarly, for any row w 2 in the matrix resulting from mode-3 matricization, there will be an identical row w 1 in the mode-1 matricization.
given a weighted tuple structure T W extracted from a corpus and subject to the constraints we just mentioned, by matricizing the corresponding labeled third-order tensor X λ we obtain the following four distinct semantic vector spaces: Words like marine and teacher are represented in the W 1 ×LW 2 space by vectors whose dimensions correspond to features such as use, gun or own, book .