645 results about "Text retrieval" patented technology

The invention provides a video content quick retrieving method based on an object tag. The method comprises the following steps: extracting and analyzing the color feature, contour feature, scene feature and character feature of a moving object in each image frame of a video; processing a plurality of pictures of known types by using the feature extraction method, and training a contour classifier and a scene classifier by using the contour features and scene features of the pictures; processing a video to be retrieved by using the feature extraction and analysis method and the classifiers soas to generate type tags of objects in each image frame of the video, wherein the type tags are used for constructing an object tag database; and retrieving a response server to search the object tagdatabase to find videos related to a query request submitted by a user, and generating an ordered result for the user to browse and refer. The method provided by the invention can be used for retrieving the video content at a speed similar to that of the conventional text retrieval only by searching the object tag database and achieving the fine granularity retrieval of the video content, so thatthe method is more accurate than the conventional method.

The invention discloses a full text retrieval system based on natural language understanding, comprising: a database server, an information receiving judging module, a natural language processing module, a retrieving module, an indexing module, an index database and a result set processing module. The system of the invention provides two resolution strategies, that is, word classification static with semantic analysis associated with automatic segmentation and expanding inquired word static according to Hownet rule for low intelligence situation of current search engine. The deployed system converts information retrieval from current key word-based layer to knowledge (or concept)-based layer; the invention is capable of using techniques such as word classification, synonym, concept search, phrase identification, etc. with understanding and processing ability to knowledge. The search engine is provided with intelligence and humanization of information service. The user is allowed using natural language for information retrieval. The invention is capable of adding user selection behavior in interactive operation mode, so as to provide more convenient, more precise search service.

The invention relates to a method and apparatus for generating translations of natural language terms from a first language to a second language. A plurality of terms are extracted from unaligned comparable corpora of the first and second languages. Comparable corpora are sets of documents in different languages that come from the same domain and have similar genre and content. Unaligned documents are not translations of one another and are not linked in any other way. By accessing monolingual thesauri of the first and second languages, a category is assigned to each extracted term. Then, category-to-category translation probabilities are estimated, and using said category-to-category translation probabilities, term-to-term translation probabilities are estimated. The invention preferably exploits class-based normalization of probability estimates, bi-directionality, and relative frequency normalization. The most important applications are cross-language text retrieval, semi-automatic bilingual thesaurus enhancement, and machine-aided human translation.

The invention discloses a modified local sensitive hash vehicle retrieval method based on multitask deep learning. A multitask end-to-end convolution neural network is used to identify a vehicle model, a vehicle system, a vehicle logo, a color and a license plate simultaneously in a subsection parallel mode. A network module for extracting vehicle image example features based on a characteristic pyramid and an algorithm by using a modified local sensitive hash sorting algorithm to sort the vehicle characteristics in a database, and a cross-modal text retrieval method when a retrieval vehicle image can not be acquired are included. The multitask end-to-end convolution neural network and the modified local sensitive hash vehicle retrieval method are provided, the automation level and the intelligence level of vehicle retrieval can be improved effectively, little storage space is used, and image retrieval requirements in a big data era are met by using a quicker retrieval speed.

The invention relates to a network information extracting and processing method, adopting artificial intelligence and natural language processing technique, able to automatically download daily up-to-date news and information from named websites, making content extraction, classification, automatic abstracting and retrenching full text, then storing the full text, and then indexing the full text for making high-efficiency full text retrieval in future.

The invention discloses a fast data and big data combined data processing method which includes steps: (1) data input of different data sources is received and is classified and transmitted according to fast data and big data; (2) fast data enter a real-time trading module which performs real-time calculation and inquiring on fast data by aid of a distributed memory; (3) a full-text retrieval module performs full-text retrieval according to the fast data result; (4) big data enter a volume historical data analysis module, are stored and are subjected to complete inquiring and statistic analysis; and (5) an application module receives data processed in the step (2), the step (3) and the step (4), and terminal display is carried out as required. The invention further provides a fast data and big data combined data processing system. The fast data and big data combined data processing method and system are low in cost and convenient to maintain, resources are distributed according to needs, and the performance is linearly expanded.

An information retrieval apparatus includes contents, an index data generating unit, a character frequency management data generating unit, a compressing/encrypting unit, a retrieval initializing unit, a full text retrieving unit, and a retrieval result displaying unit. The character frequency management data generating unit generates character frequency management data based on the contents. The compressing/encrypting unit compresses the contents and encrypts the character frequency management data. The retrieval initializing unit decrypts encrypted character frequency management data. The full text retrieving unit executes full text retrieval for compressed contents using the character frequency management data and index data when receiving a retrieval keyword. The retrieval result displaying unit decompresses a retrieval candidate selected from retrieval candidates and displays as a retrieval result.

The embodiment of the invention provides a text retrieval method and device. The text retrieval method includes the steps that an original text input by a user is acquired; retrieval words are acquired from the original text; according to the retrieval requirement of the user, the retrieval words are filtered to acquire keywords; the keywords are combined, texts in a text database are retrieved according to the combined keywords, and at least one retrieval text is acquired; the retrieval texts are displayed in a relevancy inverted order mode, and the keywords are highlighted in the retrieval texts, wherein relevancy is used for representing the relevancy degree of the original text and the retrieval texts. Due to the fact that the keywords are acquired by filtering the retrieval words according to the retrieval requirement of the user, the probability that the keywords are invalid words is reduced, and the retrieval requirement is better met compared with the manner that the retrieval words are directly acquired from the original text, the retrieval texts acquired through retrieval by the application of the combined keywords can well meet the retrieval requirement, and therefore retrieval accuracy is improved.

The invention provides a text search method. The text search method comprises the following steps that an inquiry statement is divided into a key semanteme and a key word according to the input inquiry statement, semantic extension based on the domain ontology is carried out according to the key semanteme so that the extended semanteme can be generated, an extended inquiry statement is generated according to the extended semanteme and the key word, similarity grading is carried out on the extended semanteme, semantic weighing is carried out on the extended inquiry statement, text search is carried out on the extended inquiry statement so as to obtain the corresponding inquiry result, and the searched inquiry result is displayed. According to the method, the search recall ratio and the precision ratio of a search engine can be increased in the text search process. The invention further provides a text search system.

The invention belongs to the information retrieval technology and provides a full-text retrieval system based on semantic analysis of relevant words. The full-text retrieval system based on semantic analysis of the relevant words comprises an inquiry information receiving module, a concept semantic analysis module based on the relevant words, a semantic knowledge base module, a retrieval module, an index database, an index module, a theme semantic analysis module based on the relevant words, a result set processing module and a data server. The full-text retrieval system based on semantic analysis of the relevant words is based on the improvement on a traditional Internet search engine, and by the adoption of the system, concept semantic analysis based on the relevant words and theme semantic analysis based on the relevant words of a document can be achieved, and users can obtain search results which are more accurate, more comprehensive and more intelligent.

The invention discloses an image retrieval method and an image retrieval system. The method includes for given query texts and/or query images, acquiring multiple similarity ordered lists of in-base images according to text relevance and image content relevance, and then returning a comprehensive ordered list by combining the acquired ordered lists and comprehensively considering the text similarity and the image content similarity. Through the multi-mode mixed retrieval mechanism, shortcomings of conventional single-mode retrieval mechanisms are overcome, respective advantages of a text retrieval method and an image content retrieval method are developed, and accuracy of image retrieval is greatly improved. Since only ordering results of single retrieval models are fused, the single retrieval models can be increased, decreased and replaced conveniently, text and image content feature retrieval models are configured flexibly, and performance of the image retrieval system is improved.

An information processing apparatus for creating a retrieval result displaying a list of retrieval documents is disclosed. Retrieval documents corresponding to a retrieval condition are classified into groups based on scores indicating degrees of relevance to the retrieval condition. A clustering process is conducted with respect to the retrieval documents in a group, for each of groups to which the retrieval documents belong.

The invention discloses a cryptogram-based safe full-text indexing and retrieval system. In the system, a cryptogram index library comprises a cryptogram entry reverse index and an internal document object set; a cryptogram document library is responsible for storing and managing an encrypted XML document; a word segmentation encryption server carries out Chinese word segmentation on a plaintext document and encrypts the plaintext document item by item; a cryptogram full-text indexing server standardizes an original plaintext document into an XML document, encrypts and stores the XML document in the cryptogram document library, creates a corresponding internal document object in the cryptogram index library by combining document metamessage, and creates a cryptogram reverse index for the XML document through the cryptogram entry; and a cryptogram full-text retrieval server retrieves the cryptogram index library to obtain the internal document object set through user authority information and the cryptogram entry, obtains a corresponding encrypted XML document result set from the cryptogram document library according to a pointer, decrypts the corresponding encrypted XML document result set, and returns the decrypted corresponding encrypted XML document result set to a user. The Chinese word segmentation method, the safe and high-efficiency indexing structure and the retrieval mechanism of the invention based on the special requirements of cryptogram full-text indexing can realize the cryptogram full-text indexing integrated with an access control strategy. The cryptogram-based safe full-text indexing and retrieval system has the advantages of a safe and high-efficiency indexing process, no decrypted docuterms in the indexing process, a high recall ratio and a high precision ratio in a cryptogram environment, and the like.

In various embodiments, the present invention relates disparate objects based on user behavior, thus enabling search engines to provide more comprehensive and accurate results. According to various embodiments of the present invention, multiple kinds of interactions by users with multiple classes of objects can be analyzed. The result is that disparate classes of objects can be related. Derived relations between text and objects can be used to implement search-like functionality or to extend a conventional text retrieval system.

In one embodiment, the present invention is used to improve search results and/or recommendations by employing a filtered co-occurrence matrix that provides a representation as to which queries tend to co-occur with the originally submitted query. By supplementing or replacing the original query with co-occurring queries, the system of the present invention is able to generate results that are more likely to be of interest.

The invention belongs to the technical field of cross-modal retrieval, and particularly relates to an image-text retrieval system and method based on a multi-angle self-attention mechanism. The systemcomprises a deep convolutional network, a bidirectional recurrent neural network, an image, a text self-attention network, a multi-modal space mapping network and a multi-stage training module. The deep convolutional network is used for acquiring an embedding vector of an image region feature in an image embedding space. The bidirectional recurrent neural network is used for acquiring an embedding vector of a word feature in a text space, and the two vectors are respectively input to the image and the text self-attention network. The image and text self-attention network is used for acquiringan embedded representation of an image key area and an embedded representation of key words in sentences. The multi-modal space mapping network is used for acquiring the embedded representation of the image text in the multi-modal space. The multi-stage training module is used for learning parameters in the network. A good result is obtained on a common data set Flickr30k and an MSCOCO, and the performance is greatly improved.

The invention relates to a text index online updating method in cloud environment, belonging to the technical field of computer information retrieval. After a user adds, deletes or updates a file in a text retrieval system, an index module creates incremental data of index sheets belonging to the file and merges multiple groups of incremental data of the same index sheet. A cluster main node selects first batch of nodes and second batch of nodes by sequencing child nodes according to load size and executes index updating by batch. After each batch of node receives an updating command, the retrieval service is firstly stopped, the read incremental data is merged to own index sheet, and the retrieval service is recovered. The cluster main node decides the time for starting using the retrieval service of first batch of nodes and updating the second batch of nodes according to the index service switching conditions set by the user. Finally, the cluster main node recovers the retrieval service of all nodes to complete updating. The method reduces the requirements of index updating on network bandwidth and computer resources and shortens index updating time.

The invention discloses a Titan-based enterprise information analysis platform and a construction method thereof. The Titan-based enterprise information analysis platform comprises a web crawler, a Hadoop distributed system infrastructure, a Titan server, an Elasticsearch server, a Cassandra database and an application layer, wherein the Hadoop distributed system infrastructure is used for storing collected structured or non-structured original data; the Titan server stores an enterprise relationship atlas, and utilizes the Cassandra database as a data storage medium and the Elasticsearch server as a storage medium for full-text retrievals in the enterprise relationship atlas; the application layer is used for displaying enterprise data and relationships on a front-end page by establishing a foreground application frame. The Titan-based enterprise information analysis platform and the construction method thereof provided by the invention have the advantages that through the data visualization technology, interest relationships among enterprises, such as investment relationships, can be rapidly sorted out; long-time and real-time full-automatic collection, storage and analysis can be achieved.

The invention discloses a method and software for digitalizing the full text of a standard document, belongs to the technical field of standard documents and information, solves the problems of the full text retrieval and detailed retrieval of the standard document and realizes standard information text mining. Set out from the application prospect of the standard document, processes including visualizing, characterizing and structuring are performed; the digitalization processing method is performed by a scanned image processing module, an OCR identifying and correcting module, a standard title recording module, a structured full text making module and the like; and a standard full text XML format recording and defining file and a standard full text XML file are defined. According to the standard full text XML format recording and defining file and the standard full text XML file, the method and the software define schema file development software, realize data processing of a standard title, a single-layer PDF file, a double-layer PDF file, the full text XML file, a table, an image and the like, and realize image and table retrieval and data deriving in determined ranges, such as a standard preface, a foreword, a range, referenced files, terms and the like.

The invention provides a keyword cipher text retrieval method for cloud storage. The method comprises a data owner, a cloud service sever and a data user. The data owner stores encrypted files, headlines, keywords and verify certifications of the data user and the like to the cloud service server. The cloud service server stores and builds up corresponding relationships of the encrypted files, headlines and keywords, and stores the verify certifications of the data user and the like. When the data user visits cipher texts inside the cloud service server, the data user needs the verify certification awarded by the data owner and provides the cloud service server with information like a keyword trap door and the like, the cloud service server sends the cipher texts the user needs to the data user according to the information like the keyword trap door and the like, and the data user utilizes a secret key to decipher the cipher texts. Compared with the prior art, the keyword cipher text retrieval method for the cloud storage is high in safety, and capable of lowering burdens of communication, storage, and calculation, and improving accuracy and efficiency of retrieval of cipher texts.

A new-generation industry knowledge full-text search method includes: 1, segmentation dictionary setup, namely setting up a segmentation dictionary, and storing dictionary information into a database; 2, full index setup, namely reading, dividing words and analyzing existing full-text documents called 'knowledge point documents' to set up an index file; 3, increment index setup, namely processing newly added documents and updating an index file on a hard disk; 4, memory index setup and memory segmentation dictionary setup, namely reading segmentation dictionary data in a memory to set up a memory segmentation dictionary data structure; 5, full-text search, user question standardization, word dividing, semantic comprehension, semantic extension, candidate document acquisition and candidate document ordering, wherein segmentation dictionary setup is conducted during system initialization; full index setup includes reading all knowledge documents to fully set up a hard disk index file called 'index file' for short; increment index setup is conducted when full-text files are newly added; the three events are independent from a full-text retrieval module and in independent operation.

The invention relates to a method and a system for retrieving organization names. The method comprises the following steps of: automatically segmenting all organization names into place names R, identifications I, types T and suffixes S; converting all the organization names into abbreviated letters; establishing a full-text index and language model corresponding to letter abbreviations of all the identifications I; establishing a place name dictionary, a type synonym dictionary and a suffix synonym dictionary; receiving an input letter string and segmenting into identifications I for full-text retrieval to retrieve the place names R, the types T and the suffixes S so as to obtain candidate retrieval results; and sequencing and outputting. The invention can be used for accurately and quickly retrieving Chinese and English organization names by using letters to realize the furthest fault tolerance of user inputs, shortens the information search time on the premise of no accuracy loss and also has the automatic learning function.