Answer selection method for question answering system and the system

An answer selection system for a question answering system, comprising a client and a server, wherein the client is connected with the server through a wireless network, wherein the server comprises the following modules:

• in module A, receiving a question sentence and an answer sentence input by a user, and obtaining a first word vector of each word in each sentence by using a word embedding model obtained through pre-training to calculate a combined word vector in combination with a second word vector obtained by performing fine adjustment for the word embedding model in a network training process;
• in module B, obtaining a final vector of each word in the sentence by selecting a valid feature from the connected word vectors;
• in module C, when a sentence length is greater or smaller than a preset sentence length threshold, processing the sentence by adopting a different feature extractor according to the preset sentence length threshold to generate a fitted sentence matrix representation containing contextual information;
• in module D, obtaining, by an attention pooling layer, an attention similarity of the question sentence and the answer sentence according to the sentence matrix containing contextual information obtained at block C, and performing a length-adaptive distributed representation for the question sentence and the answer sentence by extracting a maximum feature at each dimension of row and column of a similarity matrix respectively to generate a distributed vector representation for the question sentence and the answer sentence respectively;
• and
• in module E, obtaining a correlation of each answer sentence relative to the question sentence by measuring a cosine similarity between the distributed vector representations of the question sentence and the answer sentence, and sorting the answer sentences according to the correlation,
• wherein the module A comprises the following units:
• in unit A1, defining the input question sentence and the input answer sentence both as a sentence s with a length being L, obtaining a word embedding model of the t-th word Wt in the sentence s through pre-training in a corpus, and searching for a first word vector rWtfr corresponding to the word therefrom;
• and
• in unit A2, performing fine adjustment for parameters of the pre-trained word embedding model in the network training process to adapt the model to a current data set, obtaining the updated word embedding model, and obtaining a second word vector r Wtft corresponding to the word therefrom to calculate a combined word vector as rWtC=rWtfr ∥rWtft,
• wherein the module B comprises the following units:
• in unit B1, deploying a hidden layer and obtaining a final vector representation of each word in the sentence by selecting a valid feature from the connected word vectors based on rWt=tanh (Wh·rWtC+bh),
• wherein h refers to a word representation with a memory in a particular direction, WhϵD×D and bhϵD×1 both refer to hidden layer parameters, refers to a set of real numbers, D×D refers to a real number matrix with dimensions being D×D, D×1 refers to a real number matrix with dimensions being D×1, and D refers to a dimension of rWt,and
• in unit B2, obtaining a sentence distributed matrix representation containing no contextual information based on Rs=(rW1rW2 . . . rWL) by performing blocks A1, A2 and B1 for all words in all sentences.