Abstract: This application provides an apparatus and method for detecting tab folds, and an image analyzer. The apparatus for detecting tab folds includes: a first image obtaining module, configured to obtain a first image of a first lateral face of tabs of a battery cell; a second image obtaining module, configured to obtain a second image of a second lateral face of the tabs, where the second lateral face is different from the first lateral face; and an image analyzer, configured to obtain, based on the first image, a first number of layers of the tabs corresponding to the first lateral face, and obtain, based on the second image, a second number of layers of the tabs corresponding to the second lateral face, and determine, based on at least one of the first number of layers or the second number of layers, whether the tabs are in a folded state.

Abstract: A community question-answer (CQA) website answer sorting method and system combined with active learning. The sorting method comprises: step S1, performing question-answer data representation and modeling; and step S2, constructing a training set in combination with active learning, and predicting a sorting relationship of candidate question-answer pairs. Also provided is a community question-answer website answer sorting system combined with active learning. CQA website question-answer data is first represented and modeled, interference to answers sorting caused by long tail distribution of the community data is solved by means of a long tail factor, and an attention mechanism is introduced in a convolutional neural network to relieve a semantic gap problem among question-answer texts. Then, an unlabeled training set is also constructed, a sample is additionally selected from the unlabeled training set and labeled, and an answer sorting model is trained again after labeling results are merged.

Abstract: A method and device for constructing a legal knowledge graph based on joint entity and relation extraction. The construction method comprises the following steps: constructing a triple data set; design of a model architecture and training of a model, wherein the model architecture comprises an encoding layer, a head entity extraction layer and a relation-tail entity extraction layer; determination of the relation between the sentences of the text; triple combination and graph visualization. The design of the model framework of the present disclosure adopts a Chinese Bert pre-training model as an encoder. In the entity extraction part, two BiLSTM binary classifiers are used to identify the start position and end position of an entity. The head entity is first extracted, and then the tail entity corresponding to the entity relation is extracted from the extracted head entity.

Abstract: A community question-answer (CQA) web site answer sorting method and system combined with active learning. The sorting method comprises: step S1, performing question-answer data representation and modeling; and step S2, constructing a training set in combination with active learning, and predicting a sorting relationship of candidate question-answer pairs. Also provided is a community question-answer website answer sorting system combined with active learning. CQA website question-answer data is first represented and modeled, interference to answers sorting caused by long tail distribution of the community data is solved by means of a long tail factor, and an attention mechanism is introduced in a convolutional neural network to relieve a semantic gap problem among question-answer texts. Then, an unlabeled training set is also constructed, a sample is additionally selected from the unlabeled training set and labeled, and an answer sorting model is trained again after labeling results are merged.