Patent Document ID: 9665565
Application ID: 14982365

Base Claim:
1. A semantic similarity evaluation method, comprising: performing word vectorization processing separately on words in a first sentence and a word in a second sentence to obtain a first word vector and a second word vector, wherein the first sentence comprises at least two words, wherein the first word vector comprises word vectors of all words in the first sentence, wherein the second sentence comprises at least one word, and wherein the second word vector comprises word vectors of all words in the second sentence; performing, in a preset word vector compression order, compression coding processing on the first word vector according to a first compression coding parameter to obtain a first statement vector; using, when the second sentence comprises one word, the second word vector as a second statement vector; performing, in the preset word vector compression order when the second sentence comprises at least two words, compression coding processing on the second word vector according to a second compression coding parameter to obtain a second statement vector; determining a vector distance between the first statement vector and the second statement vector by obtaining, by calculating a formula sim ⁡ ( υ 1 , υ 2 ) = ( υ 1 - υ 2 ) ⁢ ( υ 1 - υ 2 )  υ 1 * υ 2  , the vector distance between the first statement vector and the second statement vector, wherein υ 1 is the first statement vector, υ 2 is the second statement vector, and sim(υ 1 ,υ 2 ) is the vector distance; evaluating a semantic similarity between the first sentence and the second sentence according to the vector distance; and providing a translated text with the first sentence to a user when the semantic similarity meets a preset condition.

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Claim 3:
3. The method according to claim 1 , further comprising perforating training on the first compression coding parameter and the second compression coding parameter using a bilingual parallel corpus, wherein the training comprises: performing word vectorization processing on a first training sentence in the bilingual parallel corpus and a second training sentence that has a same semantic meaning as the first training sentence, to obtain a first training word vector and a second training word vector; performing compression coding separately on the first training word vector and the second training word vector according to the first compression coding parameter and the second compression coding parameter, to obtain a first training statement vector of the first training sentence and a second training statement vector of the second training sentence; determining a vector distance between the first training statement vector and the second training statement vector; acquiring, when the vector distance does not meet the preset condition, a difference vector between the first training statement vector and the second training statement vector, performing error propagation on the difference vector using a back propagation algorithm for a neural network, and adjusting the first compression coding parameter and the second compression coding parameter according to the difference vector; and redetermining a vector distance between the first training statement vector and the second training statement vector using a third compression coding parameter and a fourth compression coding parameter that are obtained by means of the adjustment, and when the vector distance meets the preset condition, stopping performing training on the first compression coding parameter and the second compression coding parameter.