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2998 results about "Information extraction" patented technology

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE focus on narrowly restricted domains.

Visual and interactive wrapper generation, automated information extraction from web pages, and translation into xml

A method and a system for information extraction from Web pages formatted with markup languages such as HTML [8]. A method and system for interactively and visually describing information patterns of interest based on visualized sample Web pages [5,6,16-29]. A method and data structure for representing and storing these patterns [1]. A method and system for extracting information corresponding to a set of previously defined patterns from Web pages [2], and a method for transforming the extracted data into XML is described. Each pattern is defined via the (interactive) specification of one or more filters. Two or more filters for the same pattern contribute disjunctively to the pattern definition [3], that is, an actual pattern describes the set of all targets specified by any of its filters. A method and for extracting relevant elements from Web pages by interpreting and executing a previously defined wrapper program of the above form on an input Web page [9-14] and producing as output the extracted elements represented in a suitable data structure. A method and system for automatically translating said output into XML format by exploiting the hierarchical structure of the patterns and by using pattern names as XML tags is described.

Dynamic information extraction with self-organizing evidence construction

A data analysis system with dynamic information extraction and self-organizing evidence construction finds numerous applications in information gathering and analysis, including the extraction of targeted information from voluminous textual resources. One disclosed method involves matching text with a concept map to identify evidence relations, and organizing the evidence relations into one or more evidence structures that represent the ways in which the concept map is instantiated in the evidence relations. The text may be contained in one or more documents in electronic form, and the documents may be indexed on a paragraph level of granularity. The evidence relations may self-organize into the evidence structures, with feedback provided to the user to guide the identification of evidence relations and their self-organization into evidence structures. A method of extracting information from one or more documents in electronic form includes the steps of clustering the document into clustered text; identifying patterns in the clustered text; and matching the patterns with the concept map to identify evidence relations such that the evidence relations self-organize into evidence structures that represent the ways in which the concept map is instantiated in the evidence relations.

Medical information extraction system and method based on depth learning and distributed semantic features

ActiveCN105894088AAvoid floating point overflow problemsHigh precisionNeural learning methodsNerve networkStudy methods
he invention discloses a medical information extraction system and method based on depth learning and distributed semantic features. The system is composed of a pretreatment module, a linguistic-model-based word vector training module, a massive medical knowledge base reinforced learning module, and a depth-artificial-neural-network-based medical term entity identification module. With a depth learning method, generation of the probability of a linguistic model is used as an optimization objective; and a primary word vector is trained by using medical text big data; on the basis of the massive medical knowledge base, a second depth artificial neural network is trained, and the massive knowledge base is combined to the feature leaning process of depth learning based on depth reinforced learning, so that distributed semantic features for the medical field are obtained; and then Chinese medical term entity identification is carried out by using the depth learning method based on the optimized statement-level maximum likelihood probability. Therefore, the word vector is generated by using lots of unmarked linguistic data, so that the tedious feature selection and optimization adjustment process during medical natural language process can be avoided.
Owner:神州医疗科技股份有限公司 +1

Optical fiber radio transmission system, transmission device, and reception device

An optical fiber radio transmission system is provided which is capable of considerably improving the received dynamic range of radio signals and, in addition, is capable of optically transmitting radio signals while preventing the deterioration of transmission performance and the loss of linearity of an input signal more easily. A received level detection section 111 detects which one of predetermined levels, i.e., Level I, Level II, and Level III, the received level of a radio signal received by an antenna 400 falls under. A signal control section 112 performs an amplification/attenuation process on the radio signal in accordance with the detected level. A control information sending section 113 superimposes control information indicating the detected level on a primary signal obtained after the amplification/attenuation process. This signal is converted to an optical signal and transmitted. An optical to electrical conversion section 211 converts the optical signal received from a transmitting unit to an electrical signal. A control information extraction section 212 extracts the level from the control information, which has been superimposed on the primary signal. A signal control section 213 performs an amplification/attenuation process on the primary signal in accordance with the extracted level.

Merchandise recommending system and method thereof

The present invention relates to a merchandise recommending system, and it is an object of the present invention is to derive recommended merchandise through a multiple image search, in which image characteristic information is extracted through a text search or an image search, thereby deriving recommended merchandise. To accomplish the above object, according to one aspect of the present invention, there is provided an operator server comprising a data receiving unit for receiving a ‘merchandise search request signal’ containing a text search or an image search (request) from the user computer and receiving a unique identification number of each user input information and merchandise information together with a corresponding matching table from the manager computer, a matching process module unit for sequentially arranging images by performing a command processing on search keywords that are searched through the characteristic information extraction module unit, a merchandise recommendation module unit for deriving recommended merchandise using the image characteristic information according to a search result, a data transmission unit for transmitting the merchandise extracted through the merchandise recommendation module unit to the user computer, and a data storage unit stores the user input information, merchandise information, unique identification numbers, and matching table.

Text summarization generation system and method based on coding-decoding deep neural networks

The invention discloses a text summarization generation system and method based on coding-decoding deep neural networks. The system comprises: an Internet text obtaining module which is used for obtaining text information on the Internet; a data preprocessing module which is used for preprocessing the text information; a summarization model training module which is used for extracting quantified text information from the preprocessed text information and performing training according to a coding-decoding deep neural network model to obtain a summarization training model; a summarization generation module which is used for using the preprocessed text information as input and outputting summarization information with preset length according to the coding-decoding deep neural network model. The text summarization generation system and method based on coding-decoding deep neural networks have following advantages that text information is compressed into a summarization text with a coherent description by means of computer automatic analysis and abstraction or generation of the central content expressed by the text, which facilitate users to understand the text content and then to quickly read and select information of interest; the text is compressed into a summarization to reduce the browse burden of users.
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