Abstract: A method for training a language model based on various word vectors, a device and a medium, which relate to the field of natural language processing technologies in artificial intelligence, are disclosed. An implementation includes inputting a first sample text language material including a first word mask into the language model, and outputting a context vector of the first word mask via the language model; acquiring a first probability distribution matrix of the first word mask based on the context vector of the first word mask and a first word vector parameter matrix, and a second probability distribution matrix of the first word mask based on the context vector of the first word mask and a second word vector parameter matrix; and training the language model based on a word vector corresponding to the first word mask.

Abstract: Embodiments of the present disclosure disclose a search method and apparatus based on artificial intelligence. A specific implementation of the method comprises: acquiring at least one candidate document related to a query sentence; determining a query word vector sequence corresponding to a segmented word sequence of the query sentence, and determining a candidate document word vector sequence corresponding to a segmented word sequence of each candidate document in the at least one candidate document; performing a similarity calculation for each candidate document in the at least one candidate document; selecting, in a descending order of similarities between the candidate document and the query sentence, a preset number of candidate documents from the at least one candidate document as a search result.

Abstract: The present disclosure proposes a language generation method and apparatus. The method includes: performing encoding processing on an input sequence by using a preset encoder to generate a hidden state vector corresponding to the input sequence; in response to a granularity category of a second target segment being a phrase, decoding a first target segment vector, the hidden state vector, and a position vector corresponding to the second target segment by using N decoders to generate N second target segments; determining a loss value based on differences between respective N second target segments and a second target annotated segment; and performing parameter updating on the preset encoder, a preset classifier, and the N decoders based on the loss value to generate an updated language generation model for performing language generation.

Abstract: The present disclosure discloses a method and an apparatus for generating a text based on a semantic representation and relates to a field of natural language processing (NLP) technologies. The method for generating the text includes: obtaining an input text, the input text comprising a source text; obtaining a placeholder of an ith word to be predicted in a target text; obtaining a vector representation of the ith word to be predicted, in which the vector representation of the ith word to be predicted is obtained by calculating the placeholder of the ith word to be predicted, the source text and 1st to (i?1)th predicted words by employing a self-attention mechanism; and generating an ith predicted word based on the vector representation of the ith word to be predicted, to obtain a target text.

Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.

Abstract: An artificial intelligence based method and apparatus for generating information are disclosed. The method in an embodiment includes: segmenting a to-be-processed text into characters to obtain a character sequence; determining a character vector for each character in the character sequence to generate a character vector sequence; generating a plurality of character vector subsequences by segmenting the character vector sequence based on a preset vocabulary; for each generated character vector subsequence, determining a sum of character vectors composing the character vector subsequence as a target vector, and inputting the target vector into a pre-trained first neural network to obtain a word vector corresponding to the each character vector subsequence, the first neural network used to characterize a correspondence between the target vector and the word vector; and analyzing the to-be-processed text based on the obtained word vector to generate an analysis result.

Abstract: A character string classification method, a character string classification system, a character string classification device, and a computer readable storage medium are provided. The method includes: acquiring a to-be-classified character string, inputting the to-be-classified character string to a feature extractor to obtain a feature vector of the to-be-classified character string, and inputting the feature vector to a classifier to obtain a classification result of the to-be-classified character string. With the character string classification method, only the features of the character string itself are used in the character string classification process. That is, the to-be-classified character string is directly inputted to the feature extractor to obtain the feature vector, and the classifier classifies the to-be-classified character string based on the feature vector, thereby eliminating requirement for other information associated with the character string.

Abstract: A composition comprising a) an epoxy resin; b) a silicone resin; and c) an organosilane coupling agent having at least one protecting functional group, is disclosed. The composition can be used for coating applications.

Abstract: Embodiments of the present disclosure disclose a search method and apparatus based on artificial intelligence. A specific implementation of the method comprises: acquiring at least one candidate document related to a query sentence; determining a query word vector sequence corresponding to a segmented word sequence of the query sentence, and determining a candidate document word vector sequence corresponding to a segmented word sequence of each candidate document in the at least one candidate document; performing a similarity calculation for each candidate document in the at least one candidate document; selecting, in a descending order of similarities between the candidate document and the query sentence, a preset number of candidate documents from the at least one candidate document as a search result.

Abstract: An artificial intelligence based method and apparatus for generating information are disclosed. The method in an embodiment includes: segmenting a to-be-processed text into characters to obtain a character sequence; determining a character vector for each character in the character sequence to generate a character vector sequence; generating a plurality of character vector subsequences by segmenting the character vector sequence based on a preset vocabulary; for each generated character vector subsequence, determining a sum of character vectors composing the character vector subsequence as a target vector, and inputting the target vector into a pre-trained first neural network to obtain a word vector corresponding to the each character vector subsequence, the first neural network used to characterize a correspondence between the target vector and the word vector; and analyzing the to-be-processed text based on the obtained word vector to generate an analysis result.