258 results about "Machine reading" patented technology

The invention provides a machine reading understanding method based on a multi-head attention mechanism and dynamic iteration, and belongs to the field of natural language processing. The constructionmethod of the machine reading understanding model comprises the following steps: constructing an article and problem coding layer; constructing a recurrent neural network based on bidirectional attention flow; a self-attention layer is constructed and answer output is predicted based on a dynamic iterative decoder. According to the method, answer prediction can be carried out on questions in a machine reading understanding task text; according to the invention, a new end-to-end neural network model is established, and a new idea is provided for exploring a machine reading understanding task.

The invention discloses a machine reading model training method and device, a question and answer method and a device, and belongs to the field of natural language processing. The machine reading model training method comprises the following steps: acquiring a training sample, wherein the training sample comprises a sample question and a corresponding sample article, a real initial position and areal final position of the corresponding answer in the sample article; extracting a question feature vector of the sample question and an article feature vector of the sample article, and using a neural network structure to fuse and process the question feature vector and the article feature vector to form a fusion result; inputting the fusion result into a classifier to perform prediction of theinitial position and the final position of the answer; performing error calculation on the predicted initial position and final position, and the real initial position and the real final position of the answer; and optimizing the neural network structure according to the error calculation result. According to the machine reading model training method and device, the question and answer method anddevice in the embodiment of the invention, the corresponding answer can be directly extracted from the entire associated article through end-to-end deep learning.

The invention discloses a multi-granularity answer sorting multi-document machine reading understanding method, and belongs to the technical field of machine reading understanding application. The method is based on a pre-trained deep learning model. Splitting the document into text fragments through a sliding window, and splicing the text fragments with questions; a plurality of candidate answersgenerated by a plurality of documents are sorted by fusing multi-granularity answer sorting of statistical information, shallow semantic information, deep semantic information and answer ending wordinformation, and the semantic information of different granularities is fully utilized to capture the correlation between a question and the plurality of candidate answers. According to the method, the text representation capability and the generalization capability of a traditional machine reading understanding model are improved by utilizing a pre-trained deep learning model; Meanwhile, the defect that the input length of an existing model for a multi-document scene is limited is overcome, Meanwhile, the answer quality of multi-document machine reading understanding is improved by modeling the correlation between questions and answers from different granularities.

The invention discloses a charging processing method and system for internet sales. The method comprises the following steps: establishing a network commodity promotion service website and recording a corresponding relation between a commodity sales webpage and promoter information, seller information, commodity information, and profit allocation mode information; distributing a global unique identity ID for the corresponding relation and generating a two-dimensional code that can be read and identified by a machine or other commodity promotion carrier; receiving a purchase request that is sent out by a purchaser from a webpage of the promotion service website or receiving a purchase request that is sent by a purchaser after machine reading and identifying of the global unique identity ID of the corresponding relation, and generating an order; obtaining the promoter information, the seller information, the commodity information, and the profit allocation mode information from the promotion service website; and according to the obtained promoter information, seller information, commodity information, and order information, realizing expense collection and automatically realizing profit allocation charging between the promoter and the seller based on the profit allocation mode information.

The invention discloses a machine reading comprehension answer obtaining method based on a multi-round attention mechanism. The method comprises steps of performing word segmentation processing and vectorization processing on the questions and the texts corresponding to the questions respectively to obtain feature vectors, selecting a bidirectional long-short time memory network to encode contextsemantic information of the feature vectors, and performing modeling between the questions and the texts by using an attention mechanism to effectively capture information interaction between the questions and the texts. Attention of an article about a question is calculated through multiple rounds, context semantic information is fused, then BLSTM is used for coding the context semantic information, the above processes are repeated for multiple times, so that an nth text semantic vector is obtained, and a Self-Attention mechanism is used for obtaining a vector representation of the question;by calculating the similarity between the semantic vectors of the questions and the similarity of the semantic vectors, namely one representation of each word in the article in the question space, theaccuracy of predicting answers can be effectively improved, BLSTM and Attention are effectively combined, and the matching accuracy of the questions and the answers returned by text extraction can beimproved.

The invention discloses a machine reading understanding method, equipment, a storage medium and a device. The method comprises the steps of obtaining a to-be-understood paragraph and a plurality of corresponding target problems; and carrying out multi-thread processing on the to-be-understood paragraph and the plurality of corresponding target questions, obtaining interaction information semanticsbetween the paragraph to be understood and each target problem through an embedded layer, a coding layer and an interaction layer of the understanding model of a preset machine in sequence; based onartificial intelligence, enabling the interaction information semantics to pass through a screening layer of the preset machine reading understanding model; obtaining a valuable sentence vector whichis highly associated with each target question; and enbabling the valuable sentence vector to passes through an answer layer of the preset machine reading understanding model to obtain a predicted answer range of each target question, and performing answer prediction through the preset machine reading understanding model. The accuracy and efficiency of answer prediction are improved, the predictedanswer range is sent to a target terminal, and the user experience is improved.

The embodiment of the invention provides a question answer acquisition method and system based on machine reading understanding. The method comprises the following steps: inputting a question and a corresponding document set into a trained neural network model, and obtaining an output result of the trained neural network model; determining an answer corresponding to the question from the documentset according to the output result; wherein the trained neural network model is obtained through training according to a training set, and the training set comprises a plurality of sample problems anda sample document set and a sample label set corresponding to each sample problem. According to the method and the system provided by the embodiment of the invention, the question and the corresponding document set are input into the trained neural network model, and the answer corresponding to the question is determined from the document set according to the output result of the trained neural network model. The method has the advantages that the shortage of a machine reading understanding model of the description type problem is filled, the characteristics of multiple documents are effectively utilized, more document information is reserved, and the answer of the description type problem can be extracted more accurately.

The invention discloses a machine reading comprehension method based on multi-task joint training, and a computer storage medium. The machine reading comprehension method comprises a model construction and training stage and a test stage, wherein the model construction and training stage comprises the following steps: 1, constructing a training set; 2, establishing a machine reading comprehensionmodel which comprises a coding layer, an attention matching layer and a decoding layer; 3, dividing the training set into W sub-sample sets according to the number of samples, and training the machinereading comprehension model by using the W sub-sample sets to obtain W machine reading comprehension models; and the testing stage comprises the steps: cutting off a to-be-tested article, inputting the processed article and questions into the trained W machine reading comprehension models, obtaining W predicted answer starting positions, W predicted answer ending positions and W corresponding distribution probabilities, and calculating the average value to serve as the starting position, the ending position and the corresponding distribution probabilities of a final answer. The machine reading comprehension method can solve the problems that an existing machine reading comprehension method is low in Chinese text accuracy and the like.

The invention relates to the fields of deep learning, natural language processing and the like, and particularly relates to a syntactic relationship enhanced machine reading understanding multi-hop reasoning model and method. The model comprises a text coding module, an association element relationship graph construction module, a multi-hop reasoning module, an answer generation module and an answer prediction module. According to the invention, syntactic relationships are fused into the graph construction process, an associated element relationship graph is constructed, multi-hop reasoning iscarried out by utilizing a graph attention network based on the relationship graph, and answer support sentences are mined; meanwhile, a multi-head self-attention mechanism is introduced to further mine text clues of viewpoint type questions in the article, and an automatic solution method of the viewpoint type questions is improved; and finally, a plurality of tasks are subjected to joint optimization learning, so that when the model answers the questions, fact description for supporting the answers can be given, the interpretability of the model is improved, and meanwhile, the existing answering method for viewpoint type questions is improved.

A method of creating a visual access token is described. The method comprises: receiving an input referencing private information without revealing the private information; retrieving the referenced private information using the referencing input; encoding the retrieved private information as part of a visual image for use as a visual access token; and presenting the visual access token on a display of a portable communication device for machine reading.

The invention provides a candidate answer screening method for global machine reading comprehension modeling, which belongs to the technical field of computer information screening. All the paragraphscorresponding to a question are taken as a candidate answer fragment locating range in the method. Firstly, the F1 values between the text fragments of paragraphs are obtained, and the best candidateanswer fragment is selected by using F1; and on the other hand, after the features between the paragraphs and the question are extracted, correlation scoring is carried out by using a logistic regression model, a selected candidate answer paragraph set is obtained according to the scores, whether the paragraph where the best candidate answer fragment is located is in the candidate answer paragraph set is determined, and the paragraph where the best candidate answer fragment is located is forcibly put in the first place of the candidate answer paragraph set. Finally, the best candidate answerfragment and the candidate answer paragraph set are output. The method has the advantage that the efficiency of training and prediction is improved.

A verifiable display is provided that enables the visual content of the display to be detected and confirmed in a variety of ambient lighting conditions, enviroments, and operational states. In particular, the verifiable display has a display layer that is capable of visually setting an intended message for human or machine reading, with the intendended message being set using pixels. Depending on the operational condition of the display and the ambient light, for example, the message that is actually displayed and perceivable may vary from the intended message. To detect what message is actually displayed, a light detection layer in the verifiable display detects the illumination state of the pixels, and in that way is able to detect what message is actually being presented by the display layer.

The invention provides a two-dimensional product which can realize the storage of information data for a hand-drawing or a reference pattern and a word structure and the two-dimensional product comprises a hand-drawing product, a code generator, a decoder and a system component. The hand-drawing code technology of the invention enables information to be precisely stored in a hand-drawing or specified pattern structure and a word profile without compromising the overall characteristics and the original expressions of the original pattern or word by keeping the characteristics of the original pattern in culture and art. Therefore, it is possible to add in-depth information storage and analysis technology on the basis of meaning expressions of the pattern and word so that a user could play an important role in the design of the code sample; the generation and production of the code becomes more people oriented; and the application scope of the pattern code product is enriched with personal signature, company trademark and other word pattern. The code of the invention can be directly accessed and analyzed both online and offline. All of the patterns and words that the pattern code technology assigns are provided with the dual information attribute featuring people reading and machine reading so that the pattern information that the people read is consistent with what the machine reads on the same carrier.

The invention belongs to the technical field of natural language processing, particularly relates to a machine reading understanding method, system and device based on external knowledge enhancement,and aims to solve the problem that the existing machine reading understanding method does not utilize graph structure information among triples, so that the answer prediction accuracy is relatively low. The method of the system comprises the following steps: generating context representations of entities in a question and an original text; based on an external knowledge base, obtaining a triple set of each entity in the question and original text and a triple set of adjacent entities of each entity in the original text; based on the triple set, obtaining knowledge sub-graphs of each entity through an external knowledge graph; updating the fused knowledge sub-graph through a graph attention network to obtain knowledge representation; and splicing the context representation and the knowledgerepresentation through a sentry mechanism, and obtaining answers to the to-be-answered questions through a multilayer perceptron and a softmax classifier. According to the method, the graph structureinformation among the triples is utilized, so that the answer prediction accuracy is improved.

The invention discloses a reading understanding method and system based on collaborative attention and self-adaptive adjustment, which belong to the technical field of machine reading understanding. The method is characterized by comprising the following steps: S1, inputting a document word vector and a problem word vector, and training the document word vector and the problem word vector, the word vector comprising two granularities of character-level embedding and word embedding; s2, calculating a similarity weight of the problem and the document by using cosine similarity, and performing adaptive adjustment on document word embedding according to the similarity weight; s3, encoding the document word vector and the problem word vector through a multi-layer bidirectional gating loop unit;s4, using a collaborative attention mechanism for the document and the question, and obtaining a document vector representation query-aware with question perception and a question vector representation para-aware with document perception; s5, learning an internal dependency relationship between the document and the question by using a self-attention mechanism, and obtaining a new semantic vectorrepresentation; s6, using the attention as a pointer, predicting the starting position and the ending position of the answer, and extracting an answer sequence according to the answer span.

The invention discloses a Chinese machine-reading system. The Chinese machine-reading system comprises a data grabbing module, a data processing module, a data extracting module, a knowledge base, a data integration module and a use interface, wherein the data extracting module comprises a wiki content extracting module, a template extracting module, an entity extracting module, a relation extracting module and a template matching module. Compared with the prior art, an open extracting method is used, the extracting field is not limited, unstructured text information widely existing on the Internet can be read, and the system is suitable for being popularized and used and can automatically adapt to evolution of Chinese language.

The invention discloses an article title detection method based on a hierarchical hybrid network and a federated learning strategy, and the model comprises a title encoder which is used for carrying out feature extraction on article titles and effectively encoding article title texts into title vectors; The content encoder is used for carrying out feature extraction on the content text and effectively encoding the content text into a document vector; The associated information extractor is used for associating the title vector with the document vector by using a machine reading understanding related technology so as to obtain an associated vector of the title vector and the document vector; And the classification network is used for carrying out title codonopsis pilosula classification based on the title feature vectors, the document vectors and the association vectors, and a better title codonopsis pilosula detection effect can be obtained by utilizing the association information between the document titles and the document contents.

The invention relates to a machine reading comprehension method, an apparatus, a computer device and a storage medium. The method comprises the following steps of: obtaining a document paragraph set corresponding to a core statement from a database according to a core statement and a preset document positioning method; performing Text segmentation on the core sentence and the document paragraph set to obtain the core sentence after word segmentation and the document paragraph set after word segmentation. According to the corresponding relationship between the core sentence and the answer position, the answer paragraphs corresponding to the core sentence after word segmentation are determined from the document paragraph collection after word segmentation. The target answer paragraph corresponding to the core sentence after word segmentation is determined according to the probability value of each answer paragraph. This method can not only deal with the unstructured data, but also improve the accuracy of the target answer paragraph.

According to the multi-document-based complex problem automatic solving method provided by the invention, a strategy of reading understanding and answer sorting is adopted, and a bridging entity is introduced for a complex problem with an inference process, so that the complex problem is effectively solved. Aiming at a multi-document machine reading understanding technology, firstly, documents aresplit into paragraphs, and then answers are predicted by utilizing a reading understanding model for each paragraph, so that the recall rate of the answers is effectively improved; the plurality of candidate answers is sorted by using a sorting model, and the most possible answer is selected; for a problem with an inference process, a bridging entity is firstly predicted, and then the bridging entity is spliced to an original problem, so that the ambiguity of the inference problem is effectively solved. Through the above three steps, the method can achieve the solving of a complex problem based on a plurality of documents.