Abstract: Embodiments of this application disclose a classification model training method, a classification method, a device, and a medium. An initial classification model is first trained by using a first sample set including a large quantity of first samples, to obtain a pre-trained model, each first sample including a social text and an emoticon label corresponding to the social text; and the pre-trained model is then trained by using a second sample set including a small quantity of second samples, to obtain a social text sentiment classification model that uses a social text as an input and use a sentiment class probability distribution corresponding to the social text as an output. In this method, the model is trained by combining a large quantity of weakly supervised samples with a small quantity of supervised samples, to ensure that the model obtained through training has better model performance without increasing manually labeled samples.

Abstract: The present disclosure provides a human-machine interactive method based on artificial intelligence and a human-machine interactive device based on artificial intelligence. The method includes: receiving a query from a user; processing the query based on a pre-generated model, and obtaining an answer with a human conversation style corresponding to the query, wherein the model is generated based on a human conversation corpus; and feeding back the answer to the user.

Abstract: Embodiments of this application disclose a hyperlink processing method and apparatus. The method includes: converting first context information of a first hyperlink in a first object into a first context vector; obtaining a first input vector of the first object, the first object including information on the first hyperlink pointing to a second object; obtaining a first average vector according to the first context vector and the first input vector; adjusting at least one of the first input vector, the first context vector, or a first output vector corresponding to the second object; and calculating a similarity between the first output vector and the first average vector according to an adjustment result, and outputting the first output vector as an output vector of the second object in response to the similarity being greater than or equal to a first target threshold.

Abstract: Embodiments of this application disclose a classification model training method, a classification method, a device, and a medium. An initial classification model is first trained by using a first sample set including a large quantity of first samples, to obtain a pre-trained model, each first sample including a social text and an emoticon label corresponding to the social text; and the pre-trained model is then trained by using a second sample set including a small quantity of second samples, to obtain a social text sentiment classification model that uses a social text as an input and use a sentiment class probability distribution corresponding to the social text as an output. In this method, the model is trained by combining a large quantity of weakly supervised samples with a small quantity of supervised samples, to ensure that the model obtained through training has better model performance without increasing manually labeled samples.

Abstract: Embodiments of this application disclose a hyperlink processing method and apparatus. The method includes: converting first context information of a first hyperlink in a first object into a first context vector; obtaining a first input vector of the first object, the first object including information on the first hyperlink pointing to a second object; obtaining a first average vector according to the first context vector and the first input vector; adjusting at least one of the first input vector, the first context vector, or a first output vector corresponding to the second object; and calculating a similarity between the first output vector and the first average vector according to an adjustment result, and outputting the first output vector as an output vector of the second object in response to the similarity being greater than or equal to a first target threshold.

Abstract: The present disclosure provides a human-machine interactive method based on artificial intelligence and a human-machine interactive device based on artificial intelligence. The method includes: receiving a query from a user; processing the query based on a pre-generated model, and obtaining an answer with a human conversation style corresponding to the query, wherein the model is generated based on a human conversation corpus; and feeding back the answer to the user.