Abstract: A keyword extraction method includes: extracting candidate words from an original document to form a first word set; acquiring a first association degree between each first word thereof and the original document, and determining a second word set according to the first association degree; for each second word in the second word set, inquiring, in a word association topology, at least one node word satisfying a condition of association with the second word and forming a third word set, the word association topology indicating an association relation among multiple node words in a predetermined field; and determining a union set of the second and third word sets, acquiring a second association degree between each candidate keyword in the union set and the original document, and selecting, according to the second association degree, at least one candidate keyword from the union set, to form a keyword set of the original document.

Abstract: A method and device for keyword extraction and a storage medium. The method includes receiving, at a terminal, an original document, acquiring, at the terminal, a candidate set by extracting at least one candidate phrase from the original document, acquiring, at the terminal, an association degree between the at least one candidate phrase in the candidate set and the original document, acquiring, at the terminal, a divergence degree of the at least one candidate phrase in the candidate set, and updating, at the terminal, a key phrase set of the original document by selecting the at least one candidate phrase from the candidate set as at least one key phrase based on the association degree and the divergence degree.

Abstract: A method for training classification model is provided. The method includes: an annotated data set is processed based on a pre-trained first model, to obtain N first class probabilities, each being a probability that the annotated sample data is classified as a respective one of N classes; maximum K first class probabilities are selected from the N first class probabilities, and K first prediction labels, each corresponding to a respective one of K first class probabilities, are determined; and a second model is trained based on the annotated data set, a real label of each of the annotated sample data and the K first prediction labels of each of the annotated sample data. A classification method and device for training classification model are also provided.

Abstract: A method, apparatus, and non-transitory computer-readable storage medium for classification prediction are provided. The method for classification prediction includes obtaining a classification prediction request. The classification prediction request may include a branch identifier. The method for classification prediction may further include determining a service branch corresponding to the classification prediction request is determined from a started classification prediction service according to the branch identifier. The method for classification prediction may additionally include performing a classification prediction task based on the service branch.

Abstract: A method and device for keyword extraction and a storage medium. The method includes receiving, at a terminal, an original document, acquiring, at the terminal, a candidate set by extracting at least one candidate phrase from the original document, acquiring, at the terminal, an association degree between the at least one candidate phrase in the candidate set and the original document, acquiring, at the terminal, a divergence degree of the at least one candidate phrase in the candidate set, and updating, at the terminal, a key phrase set of the original document by selecting the at least one candidate phrase from the candidate set as at least one key phrase based on the association degree and the divergence degree.

Abstract: A keyword extraction method includes: extracting candidate words from an original document to form a first word set; acquiring the first correlation degree between each candidate word in the first word set and the original document, and based on which determining a second word set; generating predicted words forming a third word set through a prediction model; determining a union set of the second and third word sets, acquiring the second correlation degree between each of the candidate keywords in the union set and the original document, acquiring a divergence of each candidate keyword in the union set; and selecting candidate keywords from the union set as keywords based on the second correlation degree and the divergence. Keyword redundancy can be avoided through the divergence of keywords. The final keywords are not affected by the frequency of candidate words, and the expression mode of keywords can be enriched.

Abstract: A keyword extraction method includes: extracting candidate words from an original document to form a first word set; acquiring a first association degree between each first word thereof and the original document, and determining a second word set according to the first association degree; for each second word in the second word set, inquiring, in a word association topology, at least one node word satisfying a condition of association with the second word and forming a third word set, the word association topology indicating an association relation among multiple node words in a predetermined field; and determining a union set of the second and third word sets, acquiring a second association degree between each candidate keyword in the union set and the original document, and selecting, according to the second association degree, at least one candidate keyword from the union set, to form a keyword set of the original document.