136 results about "Semantic labeling" patented technology

Markup language data applied to text or data is leveraged for providing helpful actions on certain types of text or data such as names, addresses, etc. Selected portions of text or data entered into a document and any associated markup language data are passed to an action dynamically linked library (DLL) for obtaining actions associated with markup language elements applied to the text or data. The text or data may be passed to a recognizer DLL for recognition of certain data types. The recognizer DLL utilizes markup language data associated with the text or data to assist recognition and labeling of text or data. After all applicable text and/or data is recognized and labeled, an action DLL is called for actions associated with the labeled text or data.

Methods and systems are provided for processing natural language queries. Such methods and systems may receive a natural language query from a user and generate corresponding semantic tokens. Information may be retrieved from a knowledge base using the semantic tokens. Methods and systems may leverage an interpretation module to process and analyze the retrieved information in order to determine an intention associated with the natural language query. Methods and systems may leverage an actuation module to provide results to the user, which may be based on the determined intention.

Methods for recognizing strings, labeling the strings with a semantic category and providing e-commerce actions based on the category is disclosed. The semantic category may include a type label and other metadata. Recognizer plug-ins perform the recognition of particular strings in an electronic document. The recognizer plug-ins may be packaged with an application program module or they may be written by third parties to recognize particular strings that are of interest. Action plug-ins provide possible actions to be presented to the user based upon the type label associated with the string. Tradenames, trademarks, formal names or types of consumer products may be labeled and actions to buy the products may be presented. The metadata may be used to implement coupon and affiliate programs to reward frequent shoppers or frequent recommenders. Numerous other e-commerce opportunities are presented via the semantic category and the metadata.

A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.

A new method for semantic segmentation and tagging of a patent or a technical document is provided. The semantic tags are used for search and display of patents. The semantic tagging method involves creating automatic tags for preamble, elements, and sub-elements, and their respective attributes and relationships in patent claims. The tags are used in patent search to improve search performance. The tags are used in a novel user interface for viewing and analyzing one or more patents. The user interface provides a unique method to display different tags of a patent, which provides critical information towards comprehending the patent, and helps create better search queries related to the patent.

An automated technique for tagging documents includes using a semantic tagger to generate an annotation that associates a standard tag with a first text fragment of the user-defined document, wherein the tagger is trained on a standard document annotated with a standard tag, associating the first user-defined tag with a second text fragment of the user-defined document in response to the second text fragment matching a value associated with the first user-defined tag, and establishing a mapping between the standard tag and the first user-defined tag in response to existence of a requisite correlation between the standard tag and the user-defined tag. The technique may further include selecting from the user-defined document a tagged text fragment that is associated with a second user-defined tag, and providing the tagged text fragment and a standard tag associated by the mapping with the second user-defined tag to the tagger as additional training input.

The present invention provides a mechanism to use terminologies and ontologies for the purpose of indexing, annotating and semantically marking up existing collections of datasets. The invention further provides a system for incorporating terminologies, ontologies, and contextual annotation in specific domains, such as utilizing biomedical concept hierarchies in data analytics. The resulting rich structure supports specific mechanisms for data mining and machine learning.

The invention discloses a digital museum grid and the construction method thereof; the digital museum grid includes a grid portal device, a job dispatching and an execution management device, an information center, a resource retrieval service device, an ontology service device, a heterogeneous database accessing and integration device, a grid monitoring device and other devices. The method includes: establishing an ontology device to establish the instantiation relation between digital museum resource and the global ontology by semantic labeling, establishing the heterogeneous database accessing and integration device to provide a global uniform view and a uniform access interface of heterogeneous database resource for the users of grid system recourse, establishing the grid resource monitoring device to collect original state data of monitoring nodes and classify the data into uniform standard information format for visualization, establishing the grid portal device to provide grid resource accessing and service, application of execution and monitoring grid, and a service environment supporting the cooperative work of users. In the process of application, the invention utilizes grid middleware and receives job request through a grid job dispatch device to fulfill job dispatching and execution information management and to generate and manage the resource retrieval service device so as to index a grid service device through an information service device. The invention can realize the mutual communication and organic sharing of the massive digital museum resources in multidisciplinary field and eliminate the isolated island phenomena of sample information.

The invention discloses methods for labeling and searching advanced semantics of images based on network hot topics and a device. The method for labeling the advanced semantics of the images comprises the step of: searching images similar to the entity semantics of images to be labeled and accompanying texts by using an entity semantic work of the images to be labeled based on a search engine of text key words, then extracting topics from the accompanying texts, establishing association relationship among topics, among images, and between images and topics, based on the association relationship, classifying the images with similar topics and visual features into a type, and classifying similar topics corresponding to the images with the similar visual features into a type; and selecting image types which are the most similar to the images to be labeled in the visual features of the images, and taking the corresponding topics as hot topics. According to the invention, by means of the process, the advanced semantic labeling the images is realized, and in addition, the obtained advanced semantics can be used to accurately describe the images to be labeled through denoising.

A method for recognizing strings and annotating, or labeling, the strings with a type label. After the strings are annotated with a type label, application program modules may use the type label to provide users with a choice of actions. If the user's computer does not have any actions associated with a type label, the user may be provided with the option to surf to a download Uniform Resource Locator (URL) and download action plug-ins for that type label. One or more recognizer plug-ins perform the recognition of particular strings in an electronic document. The recognizer plug-ins may be packaged with an application program module or they may be written by third parties to recognize particular strings that are of interest. One or more action plug-ins provide possible actions to be presented to the user based upon the type label associated with the string.

Systems and methods for training semantic segmentation. Embodiments of the present invention include predicting semantic labeling of each pixel in each of at least one training image using a semantic segmentation model. Further included is predicting semantic boundaries at boundary pixels of objects in the at least one training image using a semantic boundary model concurrently with predicting the semantic labeling. Also included is propagating sparse labels to every pixel in the at least one training image using the predicted semantic boundaries. Additionally, the embodiments include optimizing a loss function according the predicted semantic labeling and the propagated sparse labels to concurrently train the semantic segmentation model and the semantic boundary model to accurately and efficiently generate a learned semantic segmentation model from sparsely annotated training images.

UDDI is not capable of handling semantic markups for Web services due to its flat data model and limited search capabilities. The present invention provides semantic service description and matchmaking with registries that conforms to UDDI specification. Specifically, the present invention stores complex semantic markups in UDDI data model and uses that information to perform semantic query processing. The present invention does not require any modification to the existing UDDI registries. The add-on modules reside only on clients who wish to take advantage of semantic capabilities. This approach is completely backward compatible and can integrate seamlessly into existing UDDI infrastructure.

The present invention provides a mechanism to use terminologies and ontologies for the purpose of indexing, annotating and semantically marking up existing collections of datasets. The invention further provides a system for incorporating terminologies, ontologies, and contextual annotation in specific domains, such as utilizing biomedical concept hierarchies in data analytics. The resulting rich structure supports specific mechanisms for data mining and machine learning.

The invention discloses an automatic semantic labeling method of a high resolution remote sensing image. In the automatic semantic labeling of the high resolution remote sensing image, the automatic semantic labeling method carries out modeling on multi-scale semantic information of the remote sensing image by using a level semantic model, and achieves the automatic labeling of the high resolution remote sensing image by combining a multi-instance learning method. The automatic semantic labeling method of the high resolution remote sensing image is characterized in that the level semantic model is used for achieving a modeling expression of the prior membership function of surface features; a multi-instance multi-label method is introduced to the semantic labeling of the remote sensing image, and the difficulty of labeling work is reduced; the output results of image labeling are provided in the type of the surface feature grade membership function, and the probability confidence coefficient of the labeling results is automatically provided.

One feature of the present invention uses the parsing capabilities of a structured language model in the information extraction process. During training, the structured language model is first initialized with syntactically annotated training data. The model is then trained by generating parses on semantically annotated training data enforcing annotated constituent boundaries. The syntactic labels in the parse trees generated by the parser are then replaced with joint syntactic and semantic labels. The model is then trained by generating parses on the semantically annotated training data enforcing the semantic tags or labels found in the training data. The trained model can then be used to extract information from test data using the parses generated by the model.

Application program interfaces (API) are provided for labeling strings while a user is creating a document and providing user actions based on the type of semantic label applied to the string. A recognizer API is provided and includes properties and methods or instructions which allow recognizer plug-ins to semantically label strings of text or cells or information. An action API is provided and includes properties and methods that are called upon when a user initiates particular actions such as opening a web browser, going to a particular URL, or opening an instance of a word processing or spreadsheet program. After the strings are annotated with a type label, application program modules may use the type label to provide users with a choice of actions. If the user's computer does not have any actions associated with a type label, the user may be provided with the option to surf to a download Uniform Resource Locator (URL) and download action plug-ins for that type label. One or more recognizer plug-ins perform the recognition of particular strings in an electronic document. The recognizer plug-ins may be packaged with an application program module or they may be written by third parties to recognize particular strings that are of interest. One or more action plug-ins provide possible actions to be presented to the user based upon the type label associated with the string.

The invention provides a terrain semantic perception method based on vision and vibration touch fusion. The terrain semantic perception method comprises the steps: firstly, giving an implementation method of vision three-dimensional semantic mapping based on ORB_SLAM2 and semantic segmentation; secondly, in combination with a terrain semantic classification and recognition method based on CNN-LSTM, giving a realization thought and a fusion strategy of vision/touch fusion; and finally, based on the blue whale XQ unmanned vehicle platform, the Kinect V1.0 visual sensing unit and the vibration sensing unit, carrying out algorithm testing in a real object environment. Therefore, the semantic marking precision of the method obtained by comparing a test result with a real environment can meet the application requirements; and meanwhile, whether the terrain semantic cognition is good or not can be obviously compared according to the fusion result of whether the vibration touch exists or not,so that more reliable sensing capacity can be provided for the patroller through fusion of the vibration touch and the terrain semantic cognition, and the vibration touch can still provide terrain cognition precision within a limited range even under the condition of visual failure.

The invention discloses a training method of a convolution neural network. The method includes: firstly, obtaining a gesture image to be trained; according to Mask R-CNN target detection, segmenting and extracting the gesture image to obtain the coordinates of key points corresponding to each gesture in the gesture image; for each key point, performing corresponding identification according to thevisibility of the key point, so as to obtain the marked characteristic information, wherein, the characteristic information comprises the coordinates of the key point and the corresponding visibilitymark; for each gesture image, reducing the dimensionality of the identified feature information based on a manifold learning algorithm, and obtaining the reduced dimensionality feature point distribution image. For each feature point distribution image, according to the combination of corresponding feature points in the feature point distribution image, obtaining the gesture instruction label after the gesture semantic labeling. According to the feature point distribution image and the corresponding gesture instruction label, the initial convolution neural network is trained to obtain the trained convolution neural network, which simplifies the processing complexity and improves the processing efficiency.

The invention relates to a text classification method and a text classification device. The method comprises the following steps: an NLP (Natural Language Processing) pre-processing step, wherein analysis of a natural-language processing method is carried out on user dialogue text to obtain a word set and semantic labeling results about the user dialogue text; a multi-dimensional-feature selectionstep, wherein combination is carried out for the word set and the semantic labeling results according to a plurality of rules to obtain a vectorized characterization form of semantic information contained by the user dialogue text; and a classification step, wherein probability estimation values are calculated for user dialogue classes obtained by the multi-dimensional-feature selection step. According to the text classification method and the text classification system of the invention, the advantages of counting and a deep-learning method can be integrated, and a customer demand-oriented text classification solution can be realized through multi-dimensional-feature selection.

Techniques for spatial semantic attribute matching on image regions for location identification based on a reference dataset are provided. In one aspect, a method for matching images from heterogeneous sources is provided. The method includes the steps of: (a) parsing the images into different semantic labeled regions; (b) creating a list of potential matches by matching the images based on two or more of the images having same semantic labeled regions; and (c) pruning the list of potential matches created in step (b) by taking into consideration spatial arrangements of the semantic labeled regions in the images.