Abstract: 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 ith word 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.

Abstract: 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.

Abstract: This application relates to the field of artificial intelligence, and provides a text processing method, a model training method, and an apparatus. 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.

Abstract: The present invention discloses a missing semantics complementing method in the field of natural language processing in the artificial intelligence field, including: 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.

Abstract: 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 (S300); determining a first characteristic and a second characteristic (S301); 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 (S302); obtaining each historical case in a database and a characteristic vector corresponding to each historical case (S303); 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 (S304).

Abstract: 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.

Abstract: This application provides a natural language processing method and apparatus, to accurately determine a sentiment polarity of a word.

Abstract: 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.

Abstract: 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 (S300); determining a first characteristic and a second characteristic (S301); 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 (S302); obtaining each historical case in a database and a characteristic vector corresponding to each historical case (S303); 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 (S304).