A method for training a word vector embedding model. Said method comprises multiple iterative updating, wherein any one of the iterative updating comprises: firstly determining a central word and a plurality of corresponding context words from a word sequence corresponding to a training sentence; then determining, according to a first word vector matrix, a central word vector corresponding to the central word, and determining, according to a second word vector matrix, a plurality of context word vectors corresponding to the plurality of context words; then, determining, on the basis of the similarity between the plurality of context word vectors, a plurality of attention weights corresponding thereto; using the plurality of attention weights to perform weighted summation on the plurality of context word vectors to obtain a context representation vector of the central word; then, calculating a first similarity between the central word vector and the context representation vector; and finally, with at least increasing the first similarity as a target, updating the first word vector matrix and the second word vector matrix.
TRAINING OF WORD VECTOR EMBEDDING MODEL