90 results about "Semantic enhancement" patented technology

Apparatus with accompanying subsystems and methods for developing conversational computer applications. As a user interface, the apparatus allows for a user to initiate the conversation. The apparatus also answers simple and complex questions, understands complex requests, pursues the user for further information when the request is incomplete, and in general provides customer support with a human like conversation while, at the same time, it is capable to interact with a company's proprietary database. As a development tool, the apparatus allows a software developer to implement a conversational system much faster than takes, with current commercial systems to implement basic dialog flows. The apparatus contains three major subsystems: a state transition inference engine, a heuristic answer engine and a parser generator with semantic augmentations. A main process broker controls the flow and the interaction between the different subsystems. The state transition inference engine handles requests that require processing a transaction or retrieving exact information. The heuristic answer engine answers questions that do not require exact answers but enough information to fulfill the user's request. The parser generator processes the user's natural language request, that is, it processes the syntactical structure of the natural language requests and it builds a conceptual structure of the request. After the parser generator processes the user's request, a main process broker feeds the conceptual structure to either the heuristic answer engine or to the state transition inference engine. The interaction between the main process broker and the subsystems creates a conversational environment between the user and the apparatus, while the apparatus uses information from proprietary databases to provide information, or process information, during the course of the conversation. The apparatus is equipped with a programming interface that allows implementers to declare and specify transactions based requests and answers to a multiplicity of questions. The apparatus may be used with a speech recognition interface, in which case, the apparatus improves the recognition results through the context implicitly created by the apparatus.

A disease-specific ontology crafted by a consensus of expert clinicians may be used to semantically characterize/provide semantic meaning to dynamically changing patient electronic medical record (EMR) data in critical care settings. Hierarchical, directed node-edge-node graphs (concept maps or Vmaps) developed with an end-user friendly graphical user interface and ontology editor, can be used to represent structured clinical reasoning and serve as the first step in disease-specific ontology building. Disease domain Vmaps reflecting expert clinical reasoning associated with management of acute illnesses encountered in critical care settings (e.g. ICUs) that extend core clinical ontologies, developed and reviewed by experts, are in turn extended with existing medical ontologies and automatically translated to a domain ontology processing engine. Semantically-enhanced EMR data derived from the ontology processing engine is incorporated into both real-time ‘track and trigger” rule engines and machine learning training algorithms using aggregated data. The resulting rule engines and machine-learnt models provide enhanced diagnostic and prognostic information respectively, to assist in clinical dual modes of reasoning (analytical rules and models based on experiential data) to assist in decisions associated with the specific disease in acute critical care settings.

A method and system for human posture regression based on semantic enhancement of point cloud is provided in that embodiment of the present invention. The method comprises steps extracting the point cloud features of the hand point cloud data, and classifying them point by point, obtaining semantic segmentation information of hand point cloud data, Hand-point cloud data being semantically enhancebased on semantic segmentation information, based on the hand point cloud data after semantic enhancement, and geometrically transforming the hand posture prediction result, so that that regression result of the hand posture are obtained. The method of geometric transformation of input data and output data by network learning makes the method of human pose estimation more robust to the geometric transformation of input data. The semantic information of input point cloud classification sub-network and attitude regression sub-network are fused effectively, and the performance of human pose estimation is further improved.

The invention discloses a semantic mapping and positioning method based on priori laser point cloud and depth map fusion. The method comprises the steps of S1, collecting priori laser point cloud data; S2, acquiring a depth image and an RGB image, generating RGB-D point cloud based on the depth image, and initializing and registering priori laser point cloud and RGB-D point cloud; S3, camera poseconstraints are provided by the registered prior laser point cloud to perform camera pose correction; S4, a three-dimensional geometric point cloud map is created by adopting a front and back window optimization method; S5, geometric increment segmentation is carried out on the three-dimensional geometric point cloud map, object recognition and semantic segmentation are carried out on the RGB image, geometric increment segmentation and semantic segmentation results are fused, and a 3D geometric segmentation map of semantic enhanced geometric segmentation is obtained; and S6, semantic association and segmentation probability allocation updating is carried out on the object to complete construction of a semantic map. Accumulated errors of large-scale indoor mapping and positioning can be effectively eliminated, and precision and real-time performance are high.

The invention provides a scene character recognition method and system based on a semantic enhancement encoder decoder framework, and the method comprises the steps of correcting a text in any shape on a target image into a horizontal text, and obtaining a corrected image; inputting the corrected image into a convolutional neural network to extract visual features, and extracting sequence information from the visual features by using a recurrent neural network; predicting global semantic information according to the sequence information; and initializing the state of a GRU (Gateway Recirculation Unit) based on an attention mechanism by utilizing the global semantic information, calculating an attention weight according to the visual feature and the hidden state of each decoding time of theGRU, weighting the visual feature according to the attention weight, and predicting each character on the image. According to the invention, global information can be effectively utilized to overcomethe defect that local information is used in an existing method, and meanwhile, a gap between visual information and semantic information is reduced, so that the model can better process a low-quality image.

The invention discloses a deep learning sentiment analysis model based on semantic enhancement, and the model consists of six layers: a word embedding layer, a sentiment semantic enhancement layer, aCNN convolution sampling layer, a pooling layer, an LSTM layer, and a sentiment classification layer in sequence from the bottom to the top. The word embedding layer converts words of the sentences into low-dimension word vectors; the emotion semantic enhancement layer is used for enhancing emotion semantics of the model; the CNN convolution sampling layer is used for automatically extracting wordfeatures; the pooling layer is used for reducing the dimension of the feature vector; the LSTM layer is used for capturing a long-distance dependency relationship in the statement and memorizing long-time dependency serialized information; and the sentiment classification layer adopts Softmax to perform sentiment classification. According to the method, the LSTM layer is added, so that the emotion analysis accuracy can be improved, and meanwhile, the emotion semantic enhancement layer is added, so that the emotion semantics of the model is enhanced, and the emotion analysis effect is improved; the invention further discloses a sentiment analysis method based on the deep learning sentiment analysis model, and the accuracy of Chinese short text sentiment analysis can be improved.

The invention discloses a short text classification method based on semantic enhancement. The method comprises the steps of 1, constructing a short text classifier, obtaining a short text training setrelevant to the field from internet resources, expanding corpuses and training word vectors for each short text, and training the short text classifier; 2, after expanding the corpuses and training the word vectors for each to-be-classified short text, inputting the to-be-classified short texts to the short text classifier obtained in step 1 for classification to obtain a classification result. By means of the short text classification method based on the semantic enhancement, the semantics of the short texts is enhanced and the texts are classified, aiming at the features of the short textsthat the information amount is small and the semantics is sparse, a method of expanding the corpuses with high quality and training the word vectors with high precision is utilized to conduct semanticenhancement representation on the short texts; meanwhile, an efficient text classification algorithm is utilized, the finite features of the texts are captured to the greatest extent, and the training time of the classifier is effectively shortened.

The invention discloses a method and a system for enhancing file entity association degree based on a knowledge graph. The method comprises the following steps: obtaining archive text data; identifying the archive text data by utilizing an entity identification model, and generating instance data of a defined entity; identifying the instance data of the defined entity by using a relationship extraction model, and generating a minimum unit in a knowledge graph; using a knowledge fusion model to carry out deduplication preprocessing on the minimum unit in a knowledge graph, establishing partition index sub-documents, searching for matched entities according to text similarity or structural similarity, and performing knowledge fusion through a preset entity alignment algorithm to enhance theassociation degree of archive entities. The main functions of intelligent file collection and filing, data processing and analysis and file resource semantic enhancement are achieved through entity recognition, relation extraction and fusion technologies, powerful support is provided for semantic association and intelligent development of file management, and the file data association degree and the file data utilization rate are increased.

The invention discloses a knowledge graph construction method for intelligent power grid customer service questions and answers, and the method comprises the following steps: S1, carrying out the entity and assertion recognition of a question through an LSTM model, and outputting the entity and assertion of the question; S2, performing semantic enhancement processing on the entity and the assertion to obtain a matched enhanced text, and constructing a local knowledge graph; S3, based on the matched enhanced text, performing answer query to obtain a final answer of the question; and S4, constructing an integer linear programming model according to the satisfaction of the user to the final answer and the maintenance cost of the knowledge graph, and updating the local knowledge graph. According to the method, high entity and assertion recognition accuracy can be achieved, power grid domain knowledge is automatically mapped into the local knowledge graph, online updating of the service knowledge base is rapidly achieved, and high-accuracy answers are achieved with low response delay.

The invention relates to the fields of pattern recognition, machine learning and computer vision, and provides a zero sample classification method based on semantic enhancement of encyclopedic knowledge, which aims to solve the problem that the existing zero sample image classification method cannot give consideration to both word vector language information range and processing efficiency. The zero sample classification method comprises the steps of: S1, classifying unknown-class images by means of a trained convolutional neural network classifier, and subjecting semantic features of classification result tags to convex combination to serve as semantic features of the unknown-class images; S2, and classifying the semantic features of the unknown-class images obtained in the step S1 and semantic features in a pre-constructed semantic feature space by means of a nearest neighbor classifier, so as to obtain final classes of the unknown-class images. The zero sample classification method and a zero sample classification device provided by the invention enhance the global information of word vectors, so as to improve the accuracy of image zero sample classification.

The invention discloses a target detection method based on information enhancement, and aims to overcome the defect of low precision of a single-stage detection method. According to the technical scheme, a target detection system composed of a feature extraction module, a semantic improvement module, a feature selection module, a feature fusion module and a detection module is constructed; a target detection network is trained by adopting the training data set; feature extraction, semantic improvement, feature selection and feature fusion are carried out on a single-frame image by adopting thetrained target detection system; and the position and category of the target are identified. The semantic enhancement module enriches semantic information of multi-scale features; the feature selection module enhances useful information in different scale feature maps by adopting an attention module, useless information is inhibited, and the purpose of information enhancement is achieved; the feature fusion module fuses the global semantic feature map subjected to feature selection to the multi-scale feature map, so that each feature map has more accurate position and semantic information, and the detection precision is improved.

The invention discloses a text semantic analysis method and device, and the method comprises the steps: obtaining the vector representation of a given text, and generating a coding vector of the giventext according to the vector representation; wherein the given text comprises a first text and a second text; wherein the coding vectors comprise a first coding vector and a second coding vector; generating a first attention and a second attention according to the first coding vector and the second coding vector, and generating a global information vector according to the first attention and thesecond attention; obtaining a semantic enhancement vector from the global information vector; and analyzing a target text corresponding to the second text in the first text according to the semantic enhancement vector. Compared with the prior art, the technical scheme of the invention has the advantages that the semantic enhancement vector is acquired from the global information vector, and the target text corresponding to the second text in the first text is analyzed according to the semantic enhancement vector, so that the semantic representation of the vector on the text is enriched, the calculation convergence speed is increased, and the accuracy and the efficiency are improved.

The invention provides a semantic enhanced scene text recognition method and device, and the method comprises the steps of extracting a visual feature map and a context feature sequence of a scene text image through an encoder of a scene text recognition model; and determining enhanced feature expression based on the visual feature map, the context feature sequence and the position code of the feature map; obtaining global visual information and semantic information of a scene text image; adopting a specially designed recurrent neural network unit for decoding by a decoder, wherein the unit can balance independence and correlation of context information; and performing multi-head attention operation on the implicit state vector and the expanded enhanced feature expression to obtain a local apparent feature vector. The local apparent feature vector and the hidden layer output of the recurrent neural network unit jointly participate in character prediction at the current moment, so that the correlation between semantic information and visual information is enhanced. The multi-head attention mechanism design can capture saliency information and auxiliary information of the features, so that the accuracy of a scene text recognition result is relatively high.

The invention provides a semantically-enhanced classification method for music reviews, which is used for automatically classifying the music reviews. Via the method, music review contents are automatically divided into five classes, namely an album review, an artist review, a melody review, a lyric review and an external characteristic review, and automatic classification for lots of classes of unknown music reviews can be realized by only performing manual classification on a small quantity of music reviews. The method provided by the invention has the following advantages that not only are the music reviews analysed, the accuracy of the analysis is enhanced by applying music phrases related to the reviewed object, such as music metadata and lyrics; and simultaneously, via the method provided by the invention, a method for effectively combining the outputs of two classifiers is further provided, thus remarkably enhancing the accuracy of a prediction.

The invention provides an emotion classification method. The emotion classification method comprises the steps of obtaining a word embedding matrix corresponding to a context and a word embedding matrix corresponding to a target word; according to the word embedding matrix corresponding to the context, the word embedding matrix corresponding to the target word and the first semantic activation model, obtaining context representation with enhanced target word meaning and target word representation with enhanced context semantics; obtaining context representation after semantic selection according to the context representation of the target word semantic enhancement, the target word representation of the context semantic enhancement and the semantic selection model; according to the semantic integration model, extracting syntactic representation in a syntactic dependency tree corresponding to the target sentence; and obtaining an emotion classification result corresponding to the target word according to the context representation, the syntax representation and the second semantic activation model after semantic selection. Compared with the prior art, the semantic information related to the target word in the context is fully captured, and the relationship among the context, the target word and the syntax is comprehensively considered, so that the accuracy of sentiment classification is improved.

The invention discloses a false news detection system and method based on an evidence perception hierarchical interaction attention network, which discusses more credible evidence semantics by capturing evidence fragments and fusing source credibility, and is used for discovering suspicious parts in false news so as to carry out interpretable false news detection. According to the method, suspicious parts in claim settlement can be captured, more credible evidence semantics can be explored for the parts, and the interpretability of false news detection is enhanced; the internal interaction module composed of self-attention and symmetric attention enhances deep interaction and matching between news semantics and related articles, so that key evidence fragments can be effectively obtained; the global reasoning module not only can obtain more credible evidences from key evidence fragments through gating affine transformation, but also can relieve the influence of extreme semantics on a detection result through global interactive reasoning.

The invention discloses a service discovery method combining an attention mechanism LSTM and a neural topic model. The service discovery method comprises the following steps: 1, extracting natural language vocabularies from a label of a Web service description language and preprocessing the natural language vocabularies; 2, conducting semantic enhancement on the keywords extracted in the step 1, and obtaining described topic information through processing by the neural topic model; 3, embedding the keywords in the step 1 by utilizing a pre-trained word vector; 4, on the basis of the step 2 andthe step 3, carrying out described feature extraction by combining bidirectional LSTM of an attention mechanism; and 5, on the basis of the step 4, finding k services with the highest similarity froma registration service library by calculating the similarity of the feature vectors of the query request and the service description. The method has the advantages that semantic information existingin description of Web services is processed and enhanced through external information, and services meeting user function requirements are found in a large number of registration services on the basisof semantics inquired by a user.

The invention discloses a modal independent retrieval method and system based on an intermediate text semantic enhancement space. The method comprises the following steps of obtaining the bottom features of pairs of text and image, constructing a training data set and a test data set; establishing the intermediate text semantic enhancement space based on linear discriminant analysis; calculating asimilarity matrix of image text pairs; respectively constructing a projection matrix model of an image retrieval text and a text retrieval image according to the intermediate text semantic enhancement space and the similarity matrix; using the training dataset to solve the projection matrix parameters of the retrieved text and the retrieved image respectively; and based on the projection matrix parameters of the text/text retrieval images, retrieving the text/text retrieval images by using the test data set. The method respectively learns mapping matrix for image retrieval text and retrievaltask of text retrieval image, the retrieval is more pertinent, and the retrieval accuracy is greatly improved.

The invention relates to the technical field of artificial intelligence, and discloses an intelligent voice dialogue method and device based on semantic enhancement, equipment and a medium, and the method comprises the steps: inputting to-be-recognized voice data into a preset voice recognition model, and carrying out the voice conversion of a text, and obtaining to-be-analyzed text data; inputting to-be-analyzed text data into the semantic enhancement text error correction model for semantic enhancement and error correction processing to obtain text data after error correction; inputting the text data after error correction into a preset intention recognition model for intention recognition to obtain an intention recognition result; according to the intention recognition result and the intention, performing matching with a verbal skill knowledge base to obtain target answer text data; and inputting the target answer text data into a preset speech synthesis model to carry out text-to-speech conversion so as to obtain target answer speech data. Semantic enhancement and error correction processing between text conversion and intention recognition are realized, and the accuracy of inputting the text of the preset intention recognition model is improved.

A semantic augmentation system includes a sensor with a computing system and a memory in communication with the computing system, the memory storing a plurality of endpoints. The computing system is configured to infer a first and a second semantic identity for an object, based on inputs from the sensor, project a coherent narrative and perform semantic augmentation towards a user. In further examples, the system infers a first narrative comprising two semantic identities and a second narrative wherein the system infers that a user observing view didn't infer the second semantic identity and further doesn't use the second semantic identity in the second narrative. It further, uses the corresponding narrative to remind the user to carry an item and/or credential in order to start an activity.

The invention discloses a text abstract generation method based on feature extraction and semantic enhancement, and the method comprises the following steps: introducing a feature extractor, and obtaining a feature vector of an original text through the feature extractor; respectively connecting the feature vector with an output result of the encoder in a partial connection mode and a full connection mode, and filtering noise; acquiring long-distance dependence in the sentence by using a semantic enhancer, and further enhancing semantic association; using a convolutional neural network for carrying out feature extraction on a source sequence, enabling a feature extractor to directly act on a word vector of the source sequence, and meanwhile, keeping word vector layer parameters the same asword vector layer parameters of an encoder, so that the encoding process of the encoder and the feature extraction process of the feature extractor are ensured to act on the same semantic level. According to the method, the features of the sentence are extracted by using the feature extractor and then are further fused with the result of the encoder, so that the overall structure analysis of thesentence is facilitated, the noise in the text can be filtered, and the key information is found.

The invention provides an entity linking method and device based on semantic components, and relates to the technical field of computers. The method comprises the steps of obtaining a to-be-linked entity from a medical data set; determining a standard entity candidate set of the to-be-linked entities in a medical knowledge graph, wherein the standard entity candidate set comprises a plurality of standard entities most similar to the to-be-linked entities; determining a semantic similarity score of each standard entity in the candidate set and a to-be-linked entity through a pre-trained semantic enhancement model; and linking the entity to be linked to the standard entity with the highest semantic similarity score in the candidate set. Therefore, the semantic component information can be added into the entity linking process, so that the semantic enhancement model can learn more information, and the entity linking accuracy is higher.