6 results about "Noun phrase" patented technology

The application relates to the technical field of visual language fusion positioning, and discloses a multi-scale visual positioning method and system based on semantic consistency guidance, which comprises the following steps: extracting a noun phrase based on a Stanza syntax analysis model, generating text semantic features, phrase semantic features and visual features respectively based on a pre-trained BEiT-3 image-text encoding model; calculating a text consistency constraint loss through the attention interaction of the noun phrase features and the visual features, and simultaneously generating a concept-level semantic heat map; based on the semantic heat map, applying weights corresponding to semantic responses to multi-scale visual features generated by a feature pyramid network; and according to the multi-scale visual features generated after weighting, carrying out adaptive sampling based on an offset amount on a candidate region through a deformable attention module and completing multi-scale candidate frame generation; the application solves the problem that the candidate region generation of an existing model lacks semantic directionality, and improves the problems of feature redundancy and unstable semantic alignment.

This invention provides a method and apparatus for generating training data for training machine learning models. [Solution] The method involves acquiring the original image S100, generating an image caption for the original image S211, extracting noun phrases S212, generating a first pseudo-label including a first category name and a first bounding box S214, extracting proposals corresponding to objects from the original image S221, generating a region description S222, generating a second pseudo-label with a second category name and a second bounding box S223, filtering the first pseudo-label and the second pseudo-label according to pre-set filtering conditions to generate an integrated pseudo-label S300, and annotating the original image to generate training data S400.

The method for mapping a noun phrase to a description logic concept based on epitaxy firstly exhaustively lists all text segments of the noun phrase, generates a mapping table of the text segments to resources in a knowledge base; then generates an analysis sequence according to the word segmentation, part-of-speech tagging and syntax tree of the noun phrase; and finally, according to the analysis sequence, continuously refines basic concepts generated by the indexed resources from the concept of EL++, until all words are analyzed, to obtain the description logic concept to which the noun phrase is mapped. The application can automatically process complex noun phrases containing implicit relations and generate high-quality description logic concepts through analysis of the syntax tree.

There is provided a method for generating training data for training a machine learning model by a training data generating device, comprising: (a) in response to acquiring an original image, performing sub-processes of: generating an image caption for the original image, extracting at least one noun phrase from the image caption, and performing an open vocabulary object detection on the original image, to thereby generate at least one first pseudo label and performing sub-processes of: extracting at least one proposal from the original image, generating at least one region description, and generating at least one second pseudo label; and (b) filtering the at least one first pseudo label and the at least one second pseudo label according to a preset filtering condition, to thereby generate at least one integrated pseudo label, and generating the training data by annotating the original image with the at least one integrated pseudo label.

The application provides a WHO element extraction method in an English news scene, which comprises the following steps: cleaning and preprocessing data of network news, combining an article title, a lead and an article theme into one text data, and analyzing a tree structure of the text data; extracting all nouns in all noun phrases from a subtree of the text data tree structure, screening core words, recalling a core word set, calculating a characteristic value of each core word, and extracting a first core word in a sorting result as a final core word; expanding the core word into a complete element text, traversing leaf nodes corresponding to the core word, and finding simple sentence nodes in a parent node direction, connecting all texts from the simple sentence nodes to the leaf nodes and then to intermediate links, and finally generating the element text; and the application realizes extraction of WHO elements on multiple news samples, has high accuracy, can improve a recall rate of core words, and can expand a recall set.