Huawei Technologies Co., Limited

Tsinghua University

Yasheng Wang

Yang Zhang

Jiansheng Wei

Yulong Zeng

Xiao Ling Chen

Youliang Yan

Xin Jiang

Hongbo Zhang

Liqun Deng

Zhiyuan Liu

Qun Liu

Yadao Wang

Shuzhan Bi

Zhengyan Zhang

Fanchao Qi

Anqi Cui

The technology of this application relates to a response method in a human-computer dialogue, a dialogue system, and a storage medium, and belongs to the field of artificial intelligence. In a process of a dialogue between a user and a machine, a user intent of a current dialogue is determined based on an expected user intent associated with a sentence replied by the machine to the user in a previous dialogue, so that a response is made. Because processing logic for an expected user intent is introduced, accuracy of a generated response sentence is improved.

Response method in human-computer dialogue, dialogue system, and storage medium

A text processing method, a model training method, and an apparatus related to the field of artificial intelligence is provided. The method includes: obtaining target knowledge data; processing the target knowledge data to obtain a target knowledge vector; processing to-be-processed text to obtain a target text vector; fusing the target text vector and the target knowledge vector based on a target fusion model, to obtain a fused target text vector and a fused target knowledge vector; and processing the fused target text vector and/or the fused target knowledge vector based on a target processing model, to obtain a processing result corresponding to a target task. The foregoing technical solution can improve accuracy of a result of processing a target task by the target processing model.

Text processing method, model training method, and apparatus

A missing semantics complementing method in the field of natural language processing in the artificial intelligence field is provided. The method includes: obtaining a question statement and a historical dialog statement; resolving a to-be-resolved item in the question statement based on the historical dialog statement and location information of the to-be-resolved item, to obtain a resolved question statement; determining whether a component in the question statement is ellipted, and if a component in the question statement is ellipted, complementing the ellipted component based on the historical dialog statement, to obtain a question statement after ellipsis resolution; merging the resolved question statement and the question statement after ellipsis resolution, to obtain a merged question statement; and determining a target complemented question statement from the resolved question statement, the question statement after ellipsis resolution, and the merged question statement.

Missing semantics complementing method and apparatus

A natural language processing method includes obtaining a to-be-processed phrase, where the to-be-processed phrase includes M words, determining polarity characteristic information of m to-be-processed words in the M words, where polarity characteristic information of an iword in the m to-be-processed words includes n polarity characteristic values, and each polarity characteristic value corresponds to one sentiment polarity, determining a polarity characteristic vector of the to-be-processed phrase based on the polarity characteristic information of the m to-be-processed words, where the polarity characteristic vector includes n groups of components in a one-to-one correspondence with n sentiment polarities, and determining a sentiment polarity of the to-be-processed phrase based on the polarity characteristic vector of the to-be-processed phrase using a preset classifier, and outputting the sentiment polarity.

Natural language processing method and apparatus

Embodiments of the present invention relate to the field of computer technologies, and provide a search method and apparatus to resolve a problem that a reference text, of a text in a professional field, that is determined by using the prior art has relatively low accuracy. The method includes: obtaining n named entities in a current to-be-analyzed target case (S); determining a first characteristic and a second characteristic (S); generating, based on the first characteristic and the second characteristic and according to a preset vector generation rule, a target characteristic vector corresponding to the target case (S); obtaining each historical case in a database and a characteristic vector corresponding to each historical case (S); and separately calculating a similarity between the target characteristic vector and the characteristic vector corresponding to each historical case, and selecting a historical case whose similarity result meets a preset condition as a reference case (S).

Search method and apparatus

A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.

Classification model training method and apparatus

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Shenzhen, China

Yasheng Wang