Abstract: A method for semantic recognition includes: in response to performing semantic analysis on information acquired by a terminal, a sentence to be processed is acquired. Word recognition is performed on the sentence to be processed, to obtain a plurality of words and part-of-speech information thereof. A target set update operation is determined with a pre-trained word processing model, according to a word to be processed in the set of words to be processed and part-of-speech information of the word to be processed. If a dependency relationship corresponding to the target set update operation is a first dependency relationship, through each of the plurality of preset set update operations, a respective dependency relationship of the word to be processed and a respective confidence level corresponding to the dependency relationship is determined, and a respective update of the set of words to be processed is performed.

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: An indoor navigation method is provided, including: receiving an instruction for navigation, and collecting an environment image; extracting an instruction room feature and an instruction object feature carried in the instruction, and determining a visual room feature, a visual object feature, and a view angle feature based on the environment image; fusing the instruction object feature and the visual object feature with a first knowledge graph representing an indoor object association relationship to obtain an object feature, and determining a room feature based on the visual room feature and the instruction room feature; and determining a navigation decision based on the view angle feature, the room feature, and the object feature.

Abstract: A method for optimizing a training set for text classification includes: the training set for text classification is acquired; part of samples are selected from the training set as a first initial training subset, and an incorrectly tagged sample in the first initial training subset is corrected to obtain a second initial training subset; a text classification model is trained according to the second initial training subset; the samples in the training set are predicted by the trained text classification model to obtain a prediction result; an incorrectly tagged sample set is generated according to the prediction result; a key incorrectly tagged sample is selected from the incorrectly tagged sample set, and a tag of the key incorrectly tagged sample is corrected to generate a correctly tagged sample corresponding to the key incorrectly tagged sample; and the training set is updated by using the correctly tagged sample.

Abstract: Text content is determined. The text content is input to a content classifying model. The content classifying model is adapted to determine a probability of the text content belonging to a category. An evaluated value of quality of the text content is determined according to the probability of the category and a weight of the category. The weight represents importance of the category.

Abstract: A method for determining text similarity, a storage medium and an electronic device. The method includes: obtaining a first coding sequence corresponding to the first text and a second coding sequence corresponding to the second text; obtaining a first fusion feature corresponding to the first coding sequence and a second fusion feature corresponding to the second coding sequence based on the first coding sequence and the second coding sequence; converting the first fusion feature and the second fusion feature into a corresponding first semantic feature and a second semantic feature for representing semantics, and determining a text similarity between the first text and the second text based on the first semantic feature and the second semantic feature.

Abstract: At a terminal equipment side, sentence information received by the terminal equipment is acquired. A part-of-speech label sequence of text data in the sentence information for which part-of-speech labelling is to be performed is extracted. A detection result is acquired by detecting legitimacy of the part-of-speech label sequence. When the detection result indicates that the part-of-speech label sequence is illegitimate, the part-of-speech label sequence is corrected. A corrected part-of-speech label sequence is output as a result of performing part-of-speech labelling on the text data. Semantics corresponding to the sentence information is determined according to output sentence information with part-of-speech labels.

Abstract: A method for semantic recognition includes: in response to performing semantic analysis on information acquired by a terminal, a sentence to be processed is acquired. Word recognition is performed on the sentence to be processed, to obtain a plurality of words and part-of-speech information thereof. A target set update operation is determined with a pre-trained word processing model, according to a word to be processed in the set of words to be processed and part-of-speech information of the word to be processed. If a dependency relationship corresponding to the target set update operation is a first dependency relationship, through each of the plurality of preset set update operations, a respective dependency relationship of the word to be processed and a respective confidence level corresponding to the dependency relationship is determined, and a respective update of the set of words to be processed is performed.

Abstract: A method for semantic recognition includes: in response to performing semantic analysis on information acquired by a terminal, a sentence to be processed is acquired; word recognition is performed on the sentence, to obtain a plurality of words and part-of-speech information corresponding to each of the words; a target set update operation is determined with a word processing model, according to one or more words to be input, part-of-speech information of the words to be input, and a dependency relationship of a first word. The word processing model is configured to calculate first and second feature vectors according to a word feature vector of the words to be input, a part-of-speech feature vector of the part-of-speech information and a relationship feature vector of the dependency relationship of the first word, calculate confidence levels of the preset set update operations according to the first feature vector and the second feature vector.

Abstract: The present disclosure, belonging to the technical field of natural language processing, provides a text sequence segmentation method. The method can include acquiring n segmentation sub-results of the text sequence, the n segmentation sub-results being acquired by segmenting the text sequence by n segmentation models, and processing the n segmentation sub-results by a probability determination model branch in a result combination model to acquire a segmentation probability of the each segmentation position. The method can further include processing the segmentation probability of the each segmentation position by an activation function in the result combination model to acquire a segmentation result of the text sequence. According to the present disclosure, using each segmentation position in the text sequence as a unit, segmentation results of a plurality of segmentation models are combined, such that the accuracy of segmentation of a new text can be improved.

Abstract: Provided are a method and apparatuses for training a classification neural network, a text classification method and apparatus and an electronic device. The method includes: acquiring a regression result of sample text data, which is determined based on a pre-constructed first target neural network and represents a classification trend of the sample text data; inputting the sample text data and the regression result to a second target neural network; obtaining a predicted classification result of each piece of sample text data based on the second target neural network; adjusting a parameter of the second target neural network according to a difference between the predicted classification result and a true value of a corresponding category; and obtaining a trained second target neural network after a change of network loss related to the second target neural network meets a convergence condition.

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: Text content is determined. The text content is input to a content classifying model. The content classifying model is adapted to determine a probability of the text content belonging to a category. An evaluated value of quality of the text content is determined according to the probability of the category and a weight of the category. The weight represents importance of the category.

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: The present disclosure, belonging to the technical field of natural language processing, provides a text sequence segmentation method. The method can include acquiring n segmentation sub-results of the text sequence, the n segmentation sub-results being acquired by segmenting the text sequence by n segmentation models, and processing the n segmentation sub-results by a probability determination model branch in a result combination model to acquire a segmentation probability of the each segmentation position. The method can further include processing the segmentation probability of the each segmentation position by an activation function in the result combination model to acquire a segmentation result of the text sequence. According to the present disclosure, using each segmentation position in the text sequence as a unit, segmentation results of a plurality of segmentation models are combined, such that the accuracy of segmentation of a new text can be improved.

Abstract: A method for optimizing a training set for text classification includes: the training set for text classification is acquired; part of samples are selected from the training set as a first initial training subset, and an incorrectly tagged sample in the first initial training subset is corrected to obtain a second initial training subset; a text classification model is trained according to the second initial training subset; the samples in the training set are predicted by the trained text classification model to obtain a prediction result; an incorrectly tagged sample set is generated according to the prediction result; a key incorrectly tagged sample is selected from the incorrectly tagged sample set, and a tag of the key incorrectly tagged sample is corrected to generate a correctly tagged sample corresponding to the key incorrectly tagged sample; and the training set is updated by using the correctly tagged sample.

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.