Abstract: A method and apparatus for generating text. The method comprises: receiving original text and an element tag (201); generating an encoded text feature vector, and an encoded sentence feature vector of each sentence in the original text; executing decoding steps comprising: determining a word attention weight of each word in the original text at the current moment on the basis of a hidden state vector of the decoder at the current moment and the encoded text feature vector (2031); determining a sentence attention weight of each sentence in the original text; determining a normalized word attention weight of each word in the original text at the current moment; and estimating a target word at the current moment; and generating target text on the basis of the target word output by the decoder at each moment (204).

Abstract: A method and apparatus for determining a drug code. The method may include: obtaining a specification text of a drug (201); extracting drug key information in the specification text (202); obtaining, on the basis of a pre-created code inverted index, at least one code related to the drug key information and an ingredient corresponding to each code (203); and screening the at least one code on the basis of the drug key information and the ingredient corresponding to each code to obtain an anatomical therapeutic chemical classification system code of the drug (204).

Abstract: A method and apparatus for processing model data, which relate to the technical field of artificial intelligence. The method comprises: acquiring data of at least two different modalities in a to-be-processed dataset; performing feature extraction on the data of at least two different modalities, then splicing and/or superimposing same, and obtaining a feature sequence; performing model mapping on the feature sequence, to obtain multi-modal input data adapted to an autoregressive model; and inputting the multi-modal input data into the autoregressive model, and obtaining a single-modal result outputted by the autoregressive model.

Abstract: An electronic device and method for text processing, the electronic device comprises a processor (100), and the processor is configured to: determine a correlation between a first text vector and a second text vector, wherein the first text vector and the second text vector are multi-dimensional, real number vectors generated on the basis of a same text, respectively; obtain, according to the correlation, a third text vector representing the text, wherein a vector space in which the third text vector is located is correlated to vector spaces in which first text and second text vectors are located. The electronic device and method of the present invention can be used to create a text-feature representation model which represents text features by combining a plurality of view angles, thereby improving the performance of natural language processing.

Abstract: An electronic device and method for text processing, the electronic device comprises a processor (100), and the processor is configured to: determine a correlation between a first text vector and a second text vector, wherein the first text vector and the second text vector are multi-dimensional, real number vectors generated on the basis of a same text, respectively; obtain, according to the correlation, a third text vector representing the text, wherein a vector space in which the third text vector is located is correlated to vector spaces in which first text and second text vectors are located. The electronic device and method of the present invention can be used to create a text-feature representation model which represents text features by combining a plurality of view angles, thereby improving the performance of natural language processing.

Abstract: Provided are an electronic device, a method and a training method for natural language processing. The electronic device for natural language processing includes a processor configured to: for each of words obtained by segmenting a sentence in a training data set, obtain an attention parameter representing correlation between the word and each of one or more words of other words in the sentence, where each of the words is represented by a real vector; and train, based on each of the words in the sentence, information on the attention parameter acquired for the word and label information of the sentence in the training data set, a neural network for sentence classification.

Abstract: The present disclosure provides an apparatus and method for resolving zero anaphora in Chinese language and a training method. The apparatus includes: a feature vector extracting unit, configured to extract, from an input text, feature vectors which are respectively based on candidate positions of zero pronouns, and a word pair of candidate zero pronoun category and candidate noun for each position of the candidate zero pronouns; and a classifier, configured to input the feature vectors into a joint model, so as to determine the zero pronouns in the text.

Abstract: Provided are an electronic device, a method and a training method for natural language processing. The electronic device for natural language processing includes a processor configured to: for each of words obtained by segmenting a sentence in a training data set, obtain an attention parameter representing correlation between the word and each of one or more words of other words in the sentence, where each of the words is represented by a real vector; and train, based on each of the words in the sentence, information on the attention parameter acquired for the word and label information of the sentence in the training data set, a neural network for sentence classification.

Abstract: The present disclosure provides an apparatus and method for resolving zero anaphora in Chinese language and a training method. The apparatus includes: a feature vector extracting unit, configured to extract, from an input text, feature vectors which are respectively based on candidate positions of zero pronouns, and a word pair of candidate zero pronoun category and candidate noun for each position of the candidate zero pronouns; and a classifier, configured to input the feature vectors into a joint model, so as to determine the zero pronouns in the text.