1317 results about "Semantic similarity" patented technology

The present disclosure relates to performing similarity metric analysis and data enrichment using knowledge sources. A data enrichment service can compare an input data set to reference data sets stored in a knowledge source to identify similarly related data. A similarity metric can be calculated corresponding to the semantic similarity of two or more datasets. The similarity metric can be used to identify datasets based on their metadata attributes and data values enabling easier indexing and high performance retrieval of data values. A input data set can labeled with a category based on the data set having the best match with the input data set. The similarity of an input data set with a data set provided by a knowledge source can be used to query a knowledge source to obtain additional information about the data set. The additional information can be used to provide recommendations to the user.

The present disclosure includes methods and systems for generating captions for digital images. In particular, the disclosed systems and methods can train an image encoder neural network and a sentence decoder neural network to generate a caption from an input digital image. For instance, in one or more embodiments, the disclosed systems and methods train an image encoder neural network (e.g., a character-level convolutional neural network) utilizing a semantic similarity constraint, training images, and training captions. Moreover, the disclosed systems and methods can train a sentence decoder neural network (e.g., a character-level recurrent neural network) utilizing training sentences and an adversarial classifier.

The invention discloses an image retrieval method utilizing deep semantic to rank hash codes. The method includes the following steps that partial images in a multi-label image data seat serve as a training set, and the left images serve as an image testing set; a deep convolutional neural network is utilized to construct a deep hash function; a semantic similarity rank among the images is constructed according to multi-label information of the images; the deep hash function is optimized by using an agent ranking loss function based on a triple as an actual model objective function and using a stochastic gradient-descent; the hash codes of the images are calculated through the learned deep hash function, and the images can be retrieved by testing the Hamming distance between the hash codes in the images and the hash codes of each image in the training set through calculation. The method can reserve multi-stage similarities of the multi-label images in the semantic space and express and hash the codes in combination with depth features of the learned images, thereby avoiding loss of semantic information.

The invention provides a Mongolian-Chinese machine translation method for enhancing semantic feature information based on a Transformer model. The method comprises the following steps: firstly, starting from the language characteristics of Mongolian, finding out the characteristics of the additional components of the Mongolian in terms of stem, affixes and lattices, and merging the language characteristics into the training of a model; secondly, distributed representation for measuring the similarity between the two words is taken as a research background, and the influence of depth, density and semantic coincidence degree on the concept semantic similarity is comprehensively analyzed; in the translation process, a Transformer model is adopted, and the Transformer model is a multi-layer encoder which performs position encoding by using a trigonometric function and is constructed on the basis of an enhanced multi-head attention mechanism. A decoder architecture, which completely dependson the mechanism of attention to draw the global dependency between the input and the output, eliminates recursion and convolution.

The invention belongs to the technical field of wireless communication networks and particularly discloses a crowd portrayal system and a crowd portrayal method based on a microblog label. The system comprises two main modules of a microblog label recommendation module and a label theme clustering module; the first module adopts a label recommendation algorithm covering three steps, wherein a first step is homogeneous label recommendation, a second step is co-occurrence label extension, a third step is that a semantic network is built on the basis of a Chinese mapping knowledge domain, the semantic similarity between labels is measured by using a network topology property, the labels with same or similar semantics are thus removed, and the refining property of the label used for portraying a user is ensured. According to the system and the method, the condition that the commercial application value of the label of the microblog user is wide is utilized, and the research direction is indicated for the mining algorithm of labels of internet users and the application of the Chinese mapping knowledge domain.

The invention discloses a Chinese network review emotion classification method based on an integrated study frame. According to the method, a part-of-speech combination mode, an order-preserving sub-matrix mode and a frequent word sequence mode are adopted as input characteristics, in the level of characteristics, factors of the influence of Chinese word order information, interval phrase characteristics and the sentence length are considered, and the characteristic vector sparsity problem is solved through semantic similarities; the problem that many review text characteristics exist is solved, the inter-base-classifier independence is guaranteed, and the classification performance of base classifiers is improved as much as possible; a base classifier algorithm constructed based on product attributes is adopted to comprehensively review emotion information of each attribute in a text, and then the sentence-level emotional tendency of reviews is judged, so that a final classification result is more accurate. The Chinese network review emotion classification method based on the integrated study frame is applicable to e-commerce network review emotion classification in various fields, can make a potential consumer know evaluation information of a commodity before purchase and can also make a merchant better sufficiently know the consumer's opinion, and therefore the service quality is improved.

Provided is a process including: obtaining a semantic similarity graph having nodes corresponding to documents in an analyzed corpus and edges indicating semantic similarity between pairs of the documents; for at least a plurality of nodes in the graph, evaluating accuracy of the edges based on neighboring nodes and an external corpus by performing operations including: identifying the neighboring nodes based on adjacency to the respective node in the graph; selecting documents from an external corpus based on references in the selected documents to entities mentioned in the documents of the neighboring nodes; and determining how semantically similar the respective node is to the selected documents.