7 results about "Distributed representation" patented technology

This system or method provides a reading comprehension support system that allows natural language input as query text and presents the reader with sections of the text that are highly relevant to the input text. [Solution] The reading support system includes a document reading unit 101 that reads the target document, a block division unit 103 that divides the target document into multiple blocks, a distributed representation acquisition unit 104a that acquires word distributed representations for each of the multiple blocks, a question input unit 102 that reads the question, a distributed representation acquisition unit 104b that extracts words contained in the question and acquires word distributed representations, and a similarity calculation unit 106 that compares the word distributed representations in the question and each of the multiple blocks and calculates the similarity. The similarity calculation unit searches for words that match words contained in the question from among the words contained in the block, and for the matching words, it calculates the similarity between the word distributed representation in the block and the word distributed representation in the question.

The application discloses a composition automatic scoring method based on multi-stage learning, and the method comprises the following steps: S1, extracting the shallow language features, emotional features and theme relevance features of the composition; S2, theme relevance feature extraction; S3, construction of a beautiful sentence identification model and extraction of composition style features; S4, training of a base learning machine; and S5, composition vector distributed representation and feature fusion model training and prediction. The application is applied to the field of automatic composition scoring, and a comprehensive and multi-dimensional composition scoring feature is designed for Chinese composition scoring, the detection and discovery of beautiful sentences in the composition are realized, and the beauty degree of language expression in the composition is better considered; meanwhile, the composition automatic scoring based on multi-stage learning is proposed, and multi-angle composition features are effectively combined for composition scoring.

A reference map generator clusters, into a semantic map, a set of data documents selected according to at least one criterion and associated with a medical diagnosis. A parser generates an enumeration of measurements occurring in the set of data documents. A representation generator generates for each measurement in the enumeration, a sparse distributed representation (SDR). The method includes storing, by a processor on a second computing device, in each of a plurality of memory cells on the second computing device, one of the generated SDRs. A diagnosis support module receives a document comprising a plurality of measurements. The representation generator generates a compound SDR for the document. Each of the plurality of bitwise comparison circuits determine a level of overlap between the compound SDR and the stored generated SDR. The diagnosis support module provides an identification of the medical diagnosis associated with a stored SDR.

This invention relates to a named entity recognition method based on a pre-trained language model and a progressive convolutional network, comprising the following sequential steps: encoding natural language based on the pre-trained language model to obtain a representation set LS; inputting the representation set LS into a progressive convolutional network module, and using the progressive convolutional network module to progressively fuse the encodings of adjacent layers from low to high levels to obtain an aggregated distributed representation AR that integrates the features of all layers of the pre-trained language model. c ;Utilizing the CRF model, i.e., Conditional Random Field, to decode and aggregate distributed representations (AR) c This invention achieves named entity recognition. Instead of introducing external knowledge or operations to enhance entity information and improve named entity recognition accuracy, it focuses on the results obtained using the available computing power. It utilizes the proposed progressive convolutional network module to extract the full-layer representation of the pre-trained language model, overcoming the deficiency of insufficient information mining from the pre-trained language model and reducing the complexity of introducing external data for computation.

In question answering systems, the accuracy of answers obtained using large-scale language models can be improved. [Solution] The question answering device creates a virtual question sentence from a text fragment and stores the text fragment and the virtual question sentence vector, which represents the virtual question sentence created from the text fragment in distributed representation, as one record in the database. For each record stored in the database, the question answering device calculates the similarity between the virtual question sentence vector contained in that record and the question sentence vector, which represents the input question sentence in distributed representation. Based on the similarity calculated for each record, the question answering device extracts a predetermined number of text fragments from the database, which are contained in a predetermined number of records. The question answering device generates an answer to the question sentence based on the question sentence and the predetermined number of extracted text fragments.

A method encodes compositional data structures by receiving component sparse distributed representation arrays having a predetermined array length and target sparsity level, applying position-specific permutation transformations to encode ordinal position information, combining position-encoded arrays through bitwise union to generate an intermediate array, and processing the intermediate array through a dual-phase sparsity reduction procedure comprising a coarse additive phase and a fine subtractive phase controlled by a sparsity overshoot threshold to generate a composite encoded array. The dual-phase procedure converges in substantially constant iterations for 4 or more components with final sparsity tightly controlled around the target. A decoding method applies inverse position-specific transformations to generate position-decoded arrays, computes overlap scores with candidate components through bit counting operations, and determines component identities based on threshold comparison. Scalable decoding may use triadic associative memory. Applications include searchable compression, privacy-preserving analytics, and efficient neural network embeddings.

This invention relates to the field of multi-source heterogeneous data processing technology, and discloses a multi-source heterogeneous data alignment method based on distributed representation learning. This method first establishes a multi-source heterogeneous data alignment system. The system has a built-in data access module for collecting multi-source heterogeneous data, which is then preprocessed and classified for archiving. A feature optimization module is responsible for uniformly optimizing multi-modal features. A data analysis module calculates the multi-source data feature deviation value Pc, completing cross-modal correlation analysis. A dynamic iteration module continuously corrects model parameters based on the analysis results. A credibility verification module calculates the alignment confidence score Dz and performs compliance verification and credibility rating. A distributed control module dynamically adjusts data sharding, the number of replicas, and node task allocation based on real-time calculated values, improving computational efficiency and adapting the system to large-scale scenarios. A result output module provides multiple output formats and retains logs. An operation and maintenance adaptation module realizes full-process monitoring and adaptive operation and maintenance.