318 results about "Noun" patented technology

Methods, systems and computer programs for automatic, highly accurate machine comprehension of a plurality of segments of free form unstructured text in a natural language. The system answers a plurality of complex, free-form questions asked in a natural language, based on the totality of input text. The system further uses a multi-dimensional data model to measure the total effects of actions/verbs acting on various unique nouns present in the input text. The system may convert the questions into another multi-dimensional data model and may then compare the two data models in program memory to derive the answers to the posed questions. The system may then automatically detect unknown words and optionally look them up in digital information sources, such as online dictionaries and encyclopedias, to fill in the gaps in knowledge to answer the questions with expert-like reliability.

A system and method for estimating a metric of a proposed product from a document describing the product in an arbitrary degree of detail provides accuracy comparable to a function point assessment of a fully detailed document by parsing a document into a plurality of lines in an outline form and assigning a hierarchy to each line. Values or weights are determined for each of a relatively small plurality of key words in accordance with the key word and the hierarchy level of the line in the parsed document in which the key word is found. An adjustment is made based on a category of the key word such as nouns and verbs and the hierarchy level of the lines as compared with the number of hierarchy levels found in the document. Further adjustments may be made for subject matter domain of the product and the source or author of the document.

The invention relates to a method for relationship classification with an LSTM and CNN joint model based on location. The method includes the steps of (1) preprocessing data; (2) training word vectors; (3) extracting location vectors; acquiring the location vector feature and high-dimensional location feature vector of each word in a training set, cascading the word vector of each word with the high-dimensional location feature vector thereof to obtain a joint feature; (4) building a model for a specific task; encoding contextual information and semantic information of entities by use of bidirectional LSTM; outputting the vector of the location corresponding to the marked entities, inputting the output to CNN, outputting two entity nouns and their contextual information and relational wordinformation, and inputting the entity nouns and their contextual information and relational word information into a classifier for classification; (5) training the model by use of a loss function. The method does not need to manually extract any features, the joint model does not need to use additional natural language processing tools to preprocess the data, the algorithm is simple and clear, and the best effect at present is achieved.

Unstructured electronic documents are classified for profiling and targeting users for additional relevant content. Behavioral data is gathered from user activity, and user documents and actions are categorized. Profile information is combined with collaborative and editorial data to provide users with credible information regarding products. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to how the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated to determine term vectors representing the document, and the document is categorized using the term vectors, the taxonomic nouns, or the author-generated classification.

A verbal classification system and method for the efficient sending and receiving of desired information is described. Web-site proprietors (314) and Internet marketers provide a host server (320) with descriptive data about their web-sites and messages (336) according to a predefined verbal hierarchy of nouns, verbs and modifiers. An Internet user creates a role or roles (340) that describes desired information according to the same predefined verbal hierarchy of nouns, verbs, and modifiers and transmits that role (340) to the host server (320), provides the Internet user with desired web-site addresses (336) and message by matching the role (338) to web-site and message descriptive data (336). The Internet user may access a web-site using one or more roles (340) and the web-site may provide the user with custom content by matching content (338) to the Internet user's role and roles (340). The host server (320) collects role information (340) from Internet users and web-sites to generate marketing and demographics data (346) about Internet users and web-sites (328). Internet user privacy is protected because role data (340) is encrypted and contains no personal identification information (348).

The invention relates to a data searching method and system based on semantic analysis, and belongs to the technical field of information searching. The method comprises the steps that keywords input by a user are divided into words with different parts of speech through a word dividing library, for results after the words are divided, geographical positions to be searched can be determined according to a geographical noun library if the parts of the speech are geographical names, otherwise matching is carried out by entering a phrase word library, query is further carried out based on keywords corresponding to the matched record, and words input by the user are regarded as the keywords of the inquire if matching is failed. The data searching method and system based on the semantic analysis carries out the semantic analysis based on a large number of behaviors of the user, can carry out good analysis on non-concrete nouns, verb-object phrases, modification phases and other vocabularies with generalization properties, can automatically select new words needing to be put into a library at the same time, is few in searching repeated process, high in searching efficiency and relatively precise in researching result and can meet demands of clients.

A method of storing service description documents in a computerised storage system in which each document is associated with at least one verb ontological node (204) and at least one noun ontological node (254), each verb ontological node having one or more links to other verb ontological nodes and each noun ontological node having one or more links to other noun ontological nodes whereby the verb nodes form a verb space (200) and the noun nodes form a noun space (250) and a method of retrieving service description documents from a plurality of service description documents stored in this way comprising the steps of: controlling a user interface to request from a user at least one verb request term (405) and at least one noun request term (410), associating the or each verb request term (405) with a corresponding verb node (204) and the or each noun request term (410) with a corresponding noun node (254), comparing the or each corresponding verb node (204) with the or each verb node (212, 214) associated with each of the stored service description documents, comparing the or each corresponding noun node (254) with the or each noun node (262, 266) associated with each of the stored service description documents, and selecting for retrieval zero or more of the stored service description documents on the basis of the comparison steps and controlling the user interface to inform the user of the selected documents to enable the user to retrieve one or more of the selected documents.

A text semantic analysis method and system can realize semantic analysis of text data base on lexical level and sentence level. Aiming at the semantic analysis at the lexical level, the invention firstly adopts an improved word segmentation algorithm to solve the problem that English words are segmented only by spaces. Secondly, based on word segmentation, TF-IDF modeling is performed to obtain weight value; Then the text is vectorized by weighting and summing the weight value and the word vector trained by Word2Vec, and finally the document similarity is solved. At the same time, the invention considers the contribution degree of the vocabulary to the document content and the semantic status to calculate the similarity degree of the document, the result has higher accuracy, and provide agood foundation for subsequent text clustering. The present invention extracts subject-predicate object structure based on text segmentation, part-of-speech tagging, syntactic analysis and dependencyrelation for sentence level semantic analysis. The invention realizes the extraction of subject-predicate-object structures of various sentence types in all aspects, and realizes the noun expansion function, which is more consistent with the manual extraction result.

The invention discloses a multi-dimensional geographic scene identification method fusing geographic region knowledge. The method comprises the steps of preprocessing images in a database to obtain satisfied geographic scene images; obtaining object region image blocks by utilizing a method for quickly searching for object regions in the images; pre-training the obtained object region image blocks of the geographic images by using a deep convolutional neural network, performing an accurate adjustment process until the performance of the deep convolutional neural network of the scene images is no longer improved, and fusing feature matrixes into output eigenvectors; pre-establishing a geographic entity noun keyword dictionary by acquired entity noun data in geographic scene classification, performing word segmentation on target identification result data to obtain key words in a target identification result, and establishing text features; and fusing the text features and multi-dimensional image features into eigenvectors as inputs, realizing cross-media-data identification classification, and realizing scene classification fusing geographic entity information.

The invention relates to a system suitable for a blind person to surf the Internet, comprising an automatic server news downloading system and a client application system. A server system can realize real-time news downloading, store the news into a server and realizes real-time updating; and a client system can correspondingly respond to voice input of the blind person by virtue of voice recognition and synthesis and outputs a voice. For special case of the blind person, the system realizes three core functions of the Internet, namely information acquisition, knowledge learning and interaction. By applying the system provided by the invention, the blind person can effectively listen to news by virtue of the Internet, listen to an electronic books, inquire encyclopedia nouns and report and listen to a post, wherein the news listening system can support the blind person to sequentially listen to news in programs, and the blind person can pay close attention to interested news by keyword search and related news search.

The invention relates to a method and a system for managing multiple on-line users. The method comprises the following steps: obtaining the user password; judging whether the use password comprises awakening nouns; if the use password comprises the awakening nouns, extracting the voiceprint characteristic of the use password, and comparing the obtained voiceprint characteristic with the voiceprint characteristic in a voiceprint password base; determining whether the user can execute the next operation according to the comparison result. The system comprises an obtaining unit, a voiceprint password base, a judging unit, an extracting unit, a comparing unit and a determining unit. The invention can realize the managements of instant login and authority identification for multiple special users.

The invention discloses an RNN-based automatic picture description generation method. A deep web which is well trained in advance is firstly used for image feature extraction; non-noun and non-verb components are removed for words in the sentence; an LSTM network is finally used for joint training on the image features and lexical features; during the sentence generation process, a sentence formed by nouns and verbs is generated through the inputted image and the well-trained LSTM network; and then, through large corpus on the network, the final outputted sentence is generated. Automatic recognition can be realized, a digital image uploaded by the user is understood, and a natural sentence understood by a human being is generated.

The present invention provides a technique, by speech input, to specify a location, even when a name or the like of the location is not accurately known, while driving. An in-vehicle apparatus mounted in a vehicle has a speech recognition function, and stores information shown on a signboard and a noun substitute in storage. If speech inputted from a speech input device includes a noun substitute and further includes at least a part of the information shown on the signboard, a response is decided according to the information shown on the signboard, and the decided response is outputted from an output device.

A method and a server for extracting a topic and evaluating suitability of the extracted topic are disclosed. The topic extraction server includes a text preprocessing unit configured to extract noun from a document group and remove stopword from the extracted noun, a keyword extraction unit configured to calculate a weight of a noun and extracting a keyword representing the document group, a seed selection unit configured to calculate a weight of the extracted keyword and select a seed, an initial clustering unit configured to generate one cluster including the selected seed and a keyword shown by several times in a sentence including the selected seed, and a cluster combination unit configured to extract a topic group.

A system and method for matching similar master data using associated behavioral data, for example transactional data. Matching accuracy is significantly increased. Master data may be thought of as nouns. Behavioral data is associated with master data as verbs are related to nouns. Specifically, behavioral data is data associated with an action taken by a master data object. Behavioral data may include temporal and non-temporal data. Temporal data for example may include time and/or duration that a behavior occurred. Non-temporal data may include a physical location, a product, a phone number or any other quantity other than time. Using behavioral data in duplication testing adds another dimension to de-duplication that is not achieved through comparison of master data object fields alone. For example, a duplication test on two similar “person” records with similar “name” field values is improved by comparing associated “behavior” data, (phone numbers called by each “similar person”).