Ubtech Robotics Corp Ltd

Weixing Xiong

Youjun Xiong

Li Ma

Hongtao Liao

The present disclosure discloses an entity recognition model training method and an entity recognition method as well as an apparatus using them. The entity recognition model training method includes: obtaining a training text and matching the training text with a database to obtain a plurality of matching results; processing the matching results to obtain a plurality of feature vectors corresponding to the matching results; obtaining a word vector of each word in the training text by processing the training text; and training an initial entity recognition model based on the feature vector and the word vector to obtain an entity recognition model. By using this training manner, the entity recognition model obtained can have an improved accuracy of entity recognition.

Entity recognition method and apparatus

The present disclosure provides a sentence generation method as well as a sentence generation apparatus and a smart device. The method includes: obtaining an input sentence; searching for structurally similar sentence(s) of each input sentence, where the structurally similar sentence(s) are structurally similar to the input sentence; finding semantically similar sentence(s) of the structurally similar sentence(s); parsing the input sentence and the structurally similar sentence(s) to obtain a subject block, a predicate block, and an object block to rewrite the semantically similar sentences to generate a new sentence; filtering the new sentence based on a preset filtering condition; and labeling the filtered new sentence as a semantically similar sentence of the input sentence. In this manner, a plurality of new sentences with different sentence patterns can be generated based on the same input sentence, which improves the controllability in generating the sentences and saves the labor cost therein.

Sentence generation method, sentence generation apparatus, and smart device

The present invention discloses a question answering method including obtaining a first question and a first category of the first question, combining the first question with each of preset second questions corresponding to the first category to form question groups, inputting the question groups into a trained deep retrieval matching classification model to obtain a first probability of a first classification label of each of the question groups, inputting the first question into a gradient boosting decision model to obtain a second category of the first question, obtaining a second category of the second questions, adjusting the first probability of the first classification label of each of the question groups, according to the second category of the second questions and the second category of the first question in each of the question groups, and outputting a reply according to adjusted first probabilities for solving a problem of low reply accuracy.

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Shenzhen, China

Weixing Xiong