30 results about "Analytic language" patented technology

The embodiment of the invention discloses a text abstract automatic generation method and a device based on deep learning, and a storage medium. The method comprises the following steps: constructinga Chinese corpus; preprocessing the corpus text in the corpus to extract a keyword table in the corpus text; analyzing sentence feature vectors between sentences in the corpus text and sentence feature vectors of the sentences, and constructing a feature model after sentence vectorization according to the sentence feature vectors; and training the to-be-processed text by adopting the feature modelafter sentence vectorization to establish a model, and generating a corresponding text abstract. By adopting the method and the device, the omission of valuable contents can be avoided on the premiseof ensuring the abstract generation accuracy.

The invention belongs to the field of artificial intelligence, the invention relates to a text information characterization method and a system, computer equipment and a storage medium, and the methodcomprises the steps: obtaining a to-be-analyzed corpus, carrying out the word segmentation preprocessing of the to-be-analyzed corpus, generating corresponding word vectors based on the obtained segmented words, enabling the to-be-analyzed corpus to be text information, and enabling the text information to comprise at least one statement; obtaining word vectors of segmented words contained in each statement in the to-be-analyzed corpus to obtain a word vector group of each statement, and sequentially inputting the word vectors in the word vector group into the initial sentence vector algorithm model according to a sequence to generate initial sentence vectors of the corresponding statements; and inputting the initial sentence vector into a pre-trained sentence vector model to obtain a final sentence vector of each sentence, the final sentence vector being used for representing text information, and the pre-trained sentence vector model being generated based on a context relationship of the sentences. According to the method provided by the invention, the influence caused by different semantics of words in different sentences can be avoided, and the representation of the text information is more accurate.

The invention relates to a Vietnamese multi-category word disambiguation method based on a combination method, and belongs to the technical field of natural language processing. The method comprises the following steps: firstly, extracting Vietnamese multi-category word fields from a Vietnamese text, and constructing a multi-category word field library; secondly, respectively performing maximum entropy, conditional random field and support vector machine disambiguation modeling on the multi-category word field library; disambiguating a multi-category word field testing material to be disambiguated through the three constructed statistic analysis models, and obtaining part-of-speech tags of multi-category words according to analysis results. According to the Vietnamese multi-category word disambiguation method based on the combination method, the Vietnamese multi-category words are effectively disambiguated, and power support is provided for the subsequent work such as Vietnamese part-of-speech tagging, lexical analysis, syntactic analysis, semantic analysis, information extraction, information retrieval and machine translation; the problem of poor generalization performance due to a single learner is solved.

The invention relates to a maximum entropy based Vietnamese cross ambiguity elimination method and belongs to the technical field of natural language processing. The method comprises the steps of firstly performing disambiguation modeling on Vietnamese cross ambiguity field corpora in a formed Vietnamese cross ambiguity field library to obtain a Vietnamese maximum entropy cross ambiguity elimination model; and randomly selecting test corpora from the Vietnamese cross ambiguity field corpora and performing disambiguation through the established Vietnamese maximum entropy cross ambiguity elimination model to obtain a disambiguated parameter sequence. The method effectively eliminates the ambiguities of Vietnamese cross ambiguity words and provides powerful support for work such as lexical analysis, syntactic analysis, semantic analysis, information extraction, information retrieval, machine translation and the like; at present, no related Vietnamese cross ambiguity elimination reports are discovered; and the method achieves a very good effect.

The embodiment of the invention discloses an information processing method and device, equipment and a storage medium. The method comprises the following steps: at least two candidate hot words in a to-be-analyzed phrase being extracted; calculating a first heat degree of each candidate hot word based on a Bayesian average method; calculating a second heat degree of each candidate hot word based on the Newton cooling law; performing weighted calculation on the first popularity and the second popularity of the same candidate hot word to obtain comprehensive popularity of the corresponding candidate hot word; and sorting at least two candidate hot words in the to-be-analyzed phrase according to the comprehensive heat of each candidate hot word, and determining the hot words in the to-be-analyzed phrase from the sorting result according to a set sorting sequence. According to the embodiment of the invention, the hot degree of each entry is accurately calculated by analyzing the network information, so that the hot words are mined.

The invention provides a method, device and equipment for constructing a causal relationship determination model. The method comprises the steps of obtaining an original corpus set, wherein the original corpus set comprises at least one first candidate text; screening a second candidate text in the original corpus set according to a target event causal relationship template; for each second candidate text, determining a statement vector of the second candidate text according to the word vector of each word and the word vector of each character in the second candidate text; and training a to-be-trained causal relationship determination model based on the statement vector of each second candidate text to obtain a trained causal relationship determination model. According to the invention, firstly, the words and the characters are used as representation forms for expressing semantics at the same time, the semantic expression accuracy is improved, and then the text with the recessive causal relationship can be effectively recognized by analyzing the semantics.

The invention provides an intention recognition method, system and device based on deep learning and a storage medium. The method comprises the steps of acquiring and preprocessing the statement information to be analyzed; performing word segmentation on the statement information by using a word segmentation tool; weighting all the segmented words by using a pre-trained word vector model; obtaining a mood classification corresponding to the statement information by using a pre-trained first neural network; obtaining sentence pattern classification corresponding to the sentence information by using a pre-trained second neural network; obtaining expression classifications corresponding to the statement information by using a pre-trained third neural network; and predicting the intention category of the statement information by using a pre-trained fourth neural network according to the mood classification, sentence pattern classification and expression classification of the statement information. According to the method, the feature information of the statement text information in the corresponding field does not need to be extracted, and the intention type to be expressed by the statement can be accurately determined from the statement text information by analyzing the word vector of each segmented word in the statement text information.

The invention discloses a regular intention recognition method based on natural speech processing. The method comprises the following steps: acquiring language information input by a user; processing a natural language; performing semantic analysis by using NLP; summarizing language information; semantic sentiment analysis; establishing an intention recognition regular model; extracting the regular keywords; judging a user intention according to the regular keyword; creating an intention preset model; and enabling a user to select the intention. According to the method, the user selects the intention, the establishment of the intention recognition regular model is more convenient, and the language information can be recognized and judged only according to the regular keyword information in the language information, so that the whole judgment and recognition process is more convenient, and the intention recognition result is more convenient to obtain; during intention result judgment, when different judgment results exist, the user can conveniently select the intention to be expressed by himself or herself according to the language information by creating the intention preset model, so that the intention recognition and judgment result is more accurate.

The invention relates to the field of intelligent decision in artificial intelligence, and discloses a semantic emotion analysis method, which comprises the steps of obtaining an importance metric value corresponding to each word in a to-be-analyzed statement; obtaining an implicit expression corresponding to the to-be-analyzed statement through two parallel running recurrent neural network modelsaccording to the importance metric value corresponding to each word in the to-be-analyzed statement; inputting the implicit expression corresponding to the to-be-analyzed statement and the sentence label corresponding to the to-be-analyzed statement into a semantic sentiment analysis classifier; and receiving an emotion analysis classification result of the semantic emotion analysis classifier for the to-be-analyzed statement. By introducing a self-attention mechanism, the importance of each word in the sentence is quantified through an importance measurement value, and then the meaning of the current word in the whole sentence is obtained according to the position of the important word, so that each word in the sentence and the corresponding importance measurement value thereof are fusedin the hidden state of the finally output whole sentence.

The invention provides a program vulnerability analysis method based on executable formal semantics of a Go language, which comprises the following steps of: (1) studying an expanded barkes normal form officially given by the GO language, and providing a method for converting GO [EBNF] into a general barkes normal form in combination with a BNF grammar format supportable by a K framework to obtain GO [BNF]; (2) defining an executable semantic GO [KS] of the Go language based on rewriting logic by applying a K framework according to the characteristics of the GO [BNF] and the Go language; (3) analyzing the correctness of GO [KS] by adopting a method based on test cases, separately executing the test cases in the K framework and the IntelliJ IDEA tool, and analyzing the correctness of semantics by comparing the consistency of the results of the test cases; and (4) based on the K framework, searching vulnerabilities of the Go program by applying GO [KS]. Integrity of semantics of the Go language is improved, and analysis of program vulnerabilities is realized.

The invention relates to an overhaul application structured analysis method based on topology and business feature points, which is technically characterized by comprising the following steps of: establishing a feature point knowledge base corresponding to an overhaul application according to overhaul content in a power operation process; extracting or analyzing a power failure range related to the maintenance application; analyzing feature points related to the maintenance application based on the maintenance content; analyzing possible modes based on the maintenance application feature points and the power grid topology; and performing structured analysis on the opinions of all the rooms in combination with selectable modes. The method is reasonable in design, and the barriers caused by different text writing methods in the natural language processing process are eliminated by conducting standardization processing on the text data; according to the method, the equipment range and the analysis content of the text content are limited and refined, specific text analysis grammar rules can be conveniently adopted, targeted structured analysis can be carried out on various contents of the maintenance application, the labor cost can be greatly saved, and the working efficiency is improved.

The invention discloses a neural network, a training method, an aspect-level sentiment analysis method and device and a storage medium. The neural network comprises the steps that a BERT word embedding module obtains semantic information of an input text and word vectors of all words of a to-be-analyzed statement in the input text; the first full connection layer obtains a semantic feature vector based on the semantic information; the second full connection layer obtains word feature vectors based on the word vectors; a potential Dirichlet distribution module extracts topic distribution of a statement to be analyzed and topic distribution of an aspect emotion pair; the feature fusion layer judges whether the to-be-analyzed statement is matched with the aspect emotion pair or not; a decoding marking module marks the position of the target word in the decoding sequence to obtain a marking sequence; and the output layer outputs an emotion analysis result based on the judgment result output by the feature fusion layer. And multi-task output is realized, semantic information and topic distribution are subjected to feature fusion through the feature fusion layer, vocabularies in a specific field are learned, and the sentiment classification task effect of the model is improved.

The invention provides a Chinese comment-oriented sentiment multi-tendency classification method, which comprises the following steps of: firstly, extracting morpheme words and sentiment words; secondly, constructing a similarity relationship between morpheme emotion variables; and finally, calculating a morpheme emotion close path. According to the method, morpheme emotion variables are regarded as nodes in a directed weighted acyclic graph, directed weighted relation connection is constructed between morpheme emotion nodes to serve as directed weighted link edges, and on the basis of the directed weighted link edges, an effective path meeting a certain weight condition is searched. According to the method, a directed weighted acyclic graph model is combined with emotion tendency analysis, emotion multi-tendency classification of comments is achieved through three steps of extracting various morpheme emotions of the comments, analyzing similarity relationships among the morpheme emotions and calculating a morpheme emotion close path, various attitudes expressed by a user to things are more accurately distinguished, and the opinions of the users on object attributes and features are reflected.