13082results about "Text database querying" patented technology

A system and computer-implemented is provided for displaying a configurable metric relating to an environment in a graphical display along with a value of the metric calculated over a configurable time period. The metric is used to identify events of interest in the environment based on processing real time machine data from one or more sources. The configurable metric is selected and a corresponding value is calculated based on the events of interest over the configurable time period. The value of the metric may be continuously updated in real time based on receiving additional real-time machine data and displayed in a graphical interface as time progresses. Statistical trends in the value of the metric may also be determined over the configurable time period and displayed in the graphical interface as well as an indication if the value of the metric exceeds a configurable threshold value. Further, a selection of one or more thresholds for the value of the metric may be applied and an indication displayed indicating if the threshold(s) have been exceeded.

Systems and processes for operating a digital assistant are provided. In accordance with one or more examples, a method includes, at a user device with one or more processors and memory, receiving unstructured natural language information from at least one user. The method also includes, in response to receiving the unstructured natural language information, determining whether event information is present in the unstructured natural language information. The method further includes, in accordance with a determination that event information is present within the unstructured natural language information, determining whether an agreement on an event is present in the unstructured natural language information. The method further includes, in accordance with a determination that an agreement on an event is present, determining an event type of the event and providing an event description based on the event type.

An automated name searching system incorporates an automatic name classifier and a multi-path architecture in which different algorithms are applied based on cultural identity of the query name. The name classifier operates with a preemptive list, analysis of morphological elements, length, and linguistic rules. A name regularizer produces a character based computational representation of the name. A pronunciation equivalent representation such as an IPA language representation, and language specific rules to generate name searching keys, are used in a first pass to eliminate database entries which are obviously not matches for the query name. The methods can also be implemented as a callable set of library routines including an intelligent preprocessor and a name evaluator that produces a score comparing a query name and database name, based on a variety of user-adjustable parameters. The user-controlled parameters permit tuning of the search methodologies for specific custom applications.

The present disclosure provides a semantic understanding based emoji input method and device, and relates to the input method technology field. The method includes: obtaining a text content according to an input sequence; performing word segmentation on the text content, and extracting text features based on the word segmentation result; constructing an input vector using the text features, performing classification using an emotion classification model to determine an emotion label of the text content; based on a correspondence relationship between the emotion label and emojis of various themes, respectively obtaining an emoji corresponding to the emotion label from each of the various themes; sorting the obtained emojis of the various themes, and displaying the sorted emojis as candidate options in a client. The disclosed invention facilitates users to input an emoji, enhances emoji input efficiency, and provides users with rich and wide scope of emoji resources.

Multi-level hierarchical routing matrices for pattern-recognition processors are provided. One such routing matrix may include one or more programmable and/or non-programmable connections in and between levels of the matrix. The connections may couple routing lines to feature cells, groups, rows, blocks, or any other arrangement of components of the pattern-recognition processor.

A data analysis system allows users to interact with distributed data structures stored in-memory using natural language queries. The data analysis system receives a prefix of a natural language query from the user. The data analysis system provides suggestions of terms to the user for adding to the prefix. Accordingly, the data analysis system iteratively receives longer and longer prefixes of the natural language queries until a complete natural language query is received. The data analysis system stores natural language query templates that represent natural language queries associated a particular intent. For example, a natural language query template may represent queries that compare two columns of a dataset. The data analysis system compares an input prefix of natural language with the natural language query templates to determine the suggestions. The data analysis system receives user defined metrics or attributes that can be used in the natural language queries.

A method for implementing a question answering system based on a question-answer pair comprises the following steps: question analysis, question retrieval and answer selection. After a user submits aquestion expressed in natural language to the question answering system, the question answering system uses question vectorization, keyword extraction, keyword extension and other natural language processing techniques to understand the questioning intent of a user, and then uses the engine searching method in the question-answer pair database to obtain the question-related candidate question-answer pair set, and uses a matching algorithm and sorting algorithm to accurately select the best answer from the candidate sets. The invention obtains the function of the matching degree score between the question and the answer by learning by synthesizing different algorithms and models, the method of choosing the best answer from the candidate question-answer pairs is realized, and an answer selection method based on convolution neural network and Xgboost feature fusion is completed, which provides a better method for the answer selection of the question answering system.

The present disclosure is directed toward a message classification system that allows for improved customer service through intelligent classification of social media messages. For example, the message classification system may detect one or more messages that share a common context from within a group of messages, organize the detected messages into a messaging thread, and analyze messages within the messaging thread to identify a messaging thread classification. Further, the message classification system may analyze users participating in messaging threads to determine user classifications. Using the classified messaging thread and/or user classifications, the message classification system may assist an entity in improving social media customer service.

The invention may be embodied as a method of providing information. The information may be stored as objects in a database. Such a method may use identification codes to identify pointers, and the pointers may indicate the address in a database where desired objects are stored. The invention may also be embodied as a machine having an input device, a database, a processor and executable instructions. The executable instructions may cause the processor to carry out a method according to the invention.

In the present invention, information for content, software or both that a user set as a desired operation target of a content search rank changing process is stored in a operation target management table. Information for software active during a content search processing is stored in a currently active software management table. In case the content or software relates to the retrieved content as search results is present in the operation target management table or the currently active software management table, the relevancy rating of the retrieved content is calculated by adding a prescribed value to the relevancy rating of the retrieved content. Then, the retrieved content is sorted based on the relevancy rating and outputted in order sorted at a display device.

The invention relates to a construction method of a knowledge map question answering system in the field of electric power communication based on deep learning, which comprises the following steps: Step 1, semantic analysis, that is, preprocessing a question q put forward by a user in a natural language, extracting a keyword of a user inquiry, a focus of a query sentence and the like as an entityw of a question sentence from the question q; Step 2: semantic expression, namely, vectorization of the natural language question after preprocessing, and vectorization of the candidate set of answera, are used to calculate the matching degree of the question q and the answer a; and Step 3, the answer a most matched with the question q and most accurate is found through the method of semantic matching degree calculation, query and inference, so that the score S (q, a) of the question-answer pair (q, a) is the highest. The invention studies the feasibility of the question answering system constructed by the knowledge map in the field of communication of the State Grid.

The invention relates to a network security dynamic early-warning method based on a knowledge graph. After the knowledge graph is constructed according to network security data and domain knowledge of a complicated heterogeneous environment, a graph query method toward a network security domain is used for extracting target information, through the disposal and analysis for the security data, precise description is carried out, and finally, the target information is obtained. According to the network security dynamic early-warning method based on the knowledge graph, the difficulty and complexity of network security data processing can be reduced.

The invention relates to an entity relationship joint extraction method and system based on an attention mechanism. The method comprises the following steps of: converting an entity marked in trainingdata and a triple of a relationship into a form that each word corresponds to a predefined type of tag; Mapping each word in the sentences of the training data into a corresponding word vector, inputting the word vectors into a neural network model based on an attention mechanism, and performing training through a back propagation algorithm to obtain a label prediction model; And inputting the sentences needing to be subjected to entity relationship extraction into the trained label prediction model, predicting a label corresponding to each word, and obtaining entity relationship triples existing in the sentences according to the corresponding relationship between the labels and the words in the triples. The system comprises a preprocessing module, a model training module and a result processing module. According to the method, by more effectively utilizing the key information in the sentences, the joint extraction performance of the relational entities is improved, and the method hasgood practicability.

An enterprise content management system such as an electronic contract system manages a large number of secure documents for many organizations. The search of these private documents for different organizational users with role-based access control is a challenging task. A content-based extensible mark-up language (XML)-annotated secure-index search mechanism is provided that provides an effective search and retrieval of private documents with document-level security. The search mechanism includes a document analysis framework for text analysis and annotation, a search indexer to build and incorporate document access control information directly into a search index, an XML-based search engine, and a compound query generation technique to join user role and organization information into search query. By incorporating document access information directly into the search index and combining user information in the search query, search and retrieval of private contract documents can be achieved very effectively and securely with high performance.

A method for searching a document collection includes providing an index of terms indicating the documents in which the terms appear. A first statistical distribution of each of at least some of the terms in the index and a second statistical distribution of each of at least some of the categories are estimated a over the documents in the collection. A query including one or more of the terms and a category restriction referring to at least one of the categories is accepted. A modified term distribution is produced by operating on the first estimated statistical distribution of at least one of the terms in the query using the second estimated statistical distribution of the at least one of the categories, responsively to the category restriction. The query is applied to the index so as to return a response, in which occurrences of the at least one of the terms are scored responsively to the modified term distribution.

The invention provides a deep text matching method and device based on word migration learning, and the method comprises the steps: firstly, carrying out the fusion of a BERT model and carrying out the pre-training of the BERT model during the training of a deep matching model; secondly, utilizing a pre-trained BERT model to respectively represent sentences in the input sentence pairs by using initial word vectors, and then performing similarity weighting on the sentences in the sentence pairs represented by the initial word vectors to obtain weighted sentence vectors; and finally, according to the loss value corresponding to the similarity value of the statement vector, adjusting model parameters, and carrying out text matching on the input statement by utilizing a depth matching model obtained through parameter adjustment. The parameters of the pre-trained BERT model are no longer randomly initialized parameters, and part-of-speech prediction is added into the pre-trained BERT model,so that the word vector semantic information is enriched. Therefore, semantics, represented by word vectors, of sentences in the sentence pairs are more accurate through the trained BERT model, and the matching accuracy of the trained model is promoted to be improved.

Embodiments of the invention relate to a computer-implemented method for generating explanatory data from a personalized recommendations process for a primary user based at least on stored data about the primary user. The method comprises a server computer obtaining data related to one or more users who are relevant to the primary user, then determining at least one group of users relevant to the primary user. The server computer also obtains data related to one or more entities, determines one or more entities relevant to the primary user, and associates the at least one relevant group of users with the one or more relevant entities. One or more potential candidate factors are generated. A set of factors are selected from the one or more potential candidate factors, wherein the potential candidate factors are used as explanatory data to determine recommendations to the primary user.

The invention discloses a cross-modal hash retrieval method and system for fusing supervision information. The method comprises the following steps: constructing an image network, a text network and afusion network; The training sample pairs of image and text features are obtained and input into image network and text network respectively. Taking the output characteristics of the image network and the text network as the inputs of the fusion network, and defining the output of the fusion network; Constructing an objective function for learning a unified hash code according to the output of the converged network and the similarity between pairs; Solving the objective function to obtain a unified hash code; The unified hash code is used as the supervisory information and the semantic information is combined to train the hash network of a specific mode. The invention simultaneously learns the feature representation and the hash coding based on the end-to-end depth learning framework, canmore effectively capture the correlation between different modal data, and is conducive to the improvement of the cross-modal retrieval accuracy.

The invention discloses an intelligent customer service response method and system, relates to the technical field of intelligent customer service, and can solve the problems of low response efficiency and poor user experience caused by relying on manual customer service in the prior art. The method comprises the steps of preprocessing the customer service historical chat statements to export customer service corpora, and conducting the word segmentation training on the customer service corpora to obtain a language recognition model; carrying out intention identification on corpora asked by auser to match a corresponding service module, carrying out retrieval analysis in an elasticsearch database corresponding to the service module by utilizing an intention identification result, and summarizing output associated keyword answers to form a pre-selected data set; taking a text formed after word segmentation of the question corpus of the user as an input, and carrying out Elasticsearch retrieval in a pre-selected data set to output candidate keyword answers; converting the segmented keywords and the candidate keyword answers into word vectors respectively, calculating the similaritybetween the segmented keywords and the candidate keyword answers in pairs by using a WMD algorithm, and selecting the keyword answers based on the similarity values to respond to the user.

Questions and answers associated with each other are stored in a document storage section. A clustering section classifies the answers in the document storage section into clusters based on feature vectors of the answers. When a natural language question is input by the user, a database retrieval/updating section retrieves a question similar to the input question, and presents answers associated with the retrieved question together for each cluster to the user or an expert. In addition, the database retrieval/updating section automatically updates the document storage section based on an answer selected as most appropriate by the user or the expert if selected, or based on an answer newly input by the expert if no appropriate answer is available. The natural language answer input by the expert is presented to the user as it is.