Patent ID: 11874862
Assignee: XI'AN JIAOTONG UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 8:
9. The community question-answer web site answer sorting method combined with active learning according to claim 1, wherein in step 2, the unlabeled training set is constructed according to an actual research goal, and for the target questions, a k-NN algorithm is used to search several similar questions in the data set based on an open source graph computing framework GraphLab; then, a candidate question-answer pair set of the target questions is constructed by using the similar questions and all answers to the similar questions; finally, two candidate question-answer pairs are selected each time from the candidate question-answer pair set of the target questions in a non-repetitive manner, and the target questions and the two candidate question-answer pairs are respectively formed into two triples, so that a triple pair formed by the two triples are a sample in the unlabeled training set; in addition to automatically constructing the labeled training set, active learning is applied to the answer sorting algorithm, and according to the query function, the unlabeled samples which are most helpful to improve the performance of the answer sorting model are specifically selected in the unlabeled training set to be labeled and used for model training; the query function first measures a difference between the correlation scores of two candidate question-answer pairs based on an information entropy; the smaller the difference, the larger the information entropy, and the greater the inaccuracy of the prediction results of the model; a specific calculation formula is as follows:

e(TTi′)=pTTi′·log pTTi′+(1−pTTi′)·log(1−pTTi′)

pTTi′=f(rscore(ti)−rscore(ti′))

where pTTi′ indicates the probability that the sorting labels of the triples ti and ti′ are 1; f indicates a sigmoid function; rscore(ti) indicates the correlation score of the triple ti obtained by the answer sorting model;
the query function selects samples based on the similarity between candidate answers, and the final query function is as follows:

q(TTi′)=e(TTi′)+β·sim(ai,ai′)

where ai and ai′ indicate text feature matrices representing two candidate answers; sim indicates a cosine similarity; a parameter β determines and coordinates the influence of the similarity of the candidate answers on a final query score, β=0.1;
a sum of the labeling scores of samples with the same target question is taken as the labeling score of the target question, which is calculated as follows:, q
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where mi indicates the number of question-answer triples under a target question queryi.