188 results about "Lexical item" patented technology

A reaction indicator in the form of a graphical user interface is disclosed. The reaction indicator combines sentiment and intensity data relating to an asset for use in real-time evaluation of publicly traded assets, in particular equities and commodities. The reaction indicator includes graphic objects displayed upon a monitor that depict social media market sentiment, a timeline slider object, and a vertical bar chart object. The sentiment is derived based upon pairs of lexical items in local syntactic context found in a volume of social media messages.

The invention discloses a domain-knowledge-based short text classification method and a domain-knowledge-based short text classification system used in the technical field of information. The method is used for overcoming the defect that the traditional text classification method cannot well classify short texts. Aiming at the characteristics that the short text description concept signals are relatively weak and the text features are seriously insufficient, the invention provides the short text data classification method and the text classification system suitable for commodity web page data. According to the embodiment, a commodity classifier with excellent classification effect is obtained by reforming the traditional classifier, introducing new elements and devoting to matching application of algorithm and data. The introduction of the new elements comprises the following steps of: introducing a concept of domain words and introducing the concept into the classifier so as to effectively increase the information quantity of the short texts; and performing different-lexical-item-set-based semantic analysis on the short text data, particularly the web page commodity data, and introducing the semantic analysis result into the classifier so as to introduce new information for the commodity data information and improve the accuracy of text classification.

One embodiment of the present invention is a method for lexical analysis of a character stream including: (a) generating one or more parallel property bit streams in response to the character stream; (b) generating one or more lexical item streams in response to the one or more parallel property bit streams; and (c) generating one or more token streams in response to the one or more lexical item streams.

The system for controlling and tracking medical items (830) includes one or more computers and associated data stores including information concerning authorized users, patients, medical items that have been prescribed for patients, medical items available, storage locations for medical items and events associated with receiving, dispensing, taking and returning medical items for patients. Authorized users taking medical items from storage locations are enabled to provide inputs through a display terminal (880) to indicate the taking of medical items for patients. Medical items are enabled to be taken from a plurality of storage cabinets (878). Storage cabinets are operative so that users may be guided to find a selected medical item for which corresponding information is input at an associated display terminal. Alternatively, users are enabled to gain access to the interior of the cabinet and indicate through appropriate inputs, the types and quantities of items that are being taken.

An assembly and method of identifying and visually associating individuals and medical equipment includes a computing device having a software algorithm that determines a unique combination of one or more human cognitive identifiers to avoid confusion and displays the determined selection on an electronic skin located on the individual or medical equipment so that identities and associations amongst individuals and equipment can be understood readily.

The invention discloses a keyword recommending method and device. The method and device can improve correlation of keywords and published information. The method includes the steps that input information is received; the input information is segmented into a plurality of lexical items; an inverted index structure which is built in advance is inquired by means of each lexical item, and the keywords obtained by inquiring the inverted index structure through all the lexical items form a candidate recommendation word set; the correlation score of each keyword in the candidate recommendation work set and the input information is calculated, and one or more keywords are selected according to the correlation scores to serve as recommendation word to be output. The keyword recommending device comprises a receiving module, a pre-processing module, a recall module and a keyword evaluation module. By means of correlation scoring, one or more keywords which are most correlated are selected to serve as recommendation words according to the correlation scores, and the problem that in the prior art, the keywords are not sufficient in correlation is solved.

The multi-platform visual pronunciation dictionary is capable of cross-referencing words and phrases between a user's native language and a foreign language by presenting to the user a correct translation and pronunciation in a recorded video presentation by a native speaker of the foreign language. Monolinguistic cross-referencing may also be provided. The dictionary provides a user interface and lexical database designed to enable the learner to visualize and hear the target language. An electronic dictionary is provided and includes an interface with a visual display capable of playing high quality recordings showing a model speaker's face speaking the lexical item. The visual pronunciation dictionary has a plurality of high-quality synchronized video and sound recordings of a plurality of lexical items in a language spoken by a native speaker that is stored in a database and accessible by a user interface device. A dedicated SD-video-capable electronic dictionary may also be provided.

A sentiment calculator uses social media messages for the real-time evaluation of publicly assets, in particular traded equities and commodities wherein a sentiment is an integer computed based upon pairs of lexical items in local syntactic context. The sentiment calculator includes a mechanism for determining polarity in social media messages and a mechanism for determining a strength value of lexical items used in social media messages.

The dynamic programming technique employs a lexical tree that is encoded in computer memory as a flat representation in which the nodes of each generation occupy contiguous memory locations. The traversal algorithm employs a set of traversal rules whereby nodes of a given generation are processed before the parent nodes of that generation. The deepest child generation is processed first and traversal among nodes of each generation proceeds in the same topological direction.

Systems and methods for filling a customer order. An automated label and verification machine is used to print and apply patient labels to products containing medical items and then verify the product label and patient label on each of the products. In order to supply the automated label and verification machine with a batch of different products making up the customer order, a user is sent to a storage carousel and prompted to pick the products from storage locations rotated into location adjacent to a door in the cage surrounding the storage carousel. The use of the storage carousels maximizes the number of distinct products and amount of those products that may be stored in close relation to the automated label and verification machine, thereby making the process more space and time efficient.

The invention relates to the text analysis field, particularly to a semantic characteristic-based text semantic similarity analysis method. According to the technical scheme, the similarity degree between texts is analyzed more accurately and effectively by calculation based on semantic relations of internal words of the texts. According to the method, shallow analysis on association relation between texts and between lexical items is performed through singular value decomposition; a lexical item-theme set is constructed by a bayesian network; the semantic similarity between the lexical items is calculated by mutual information and context; and finally, the text similarity is calculated through a graph structure. By adoption of the text semantic similarity analysis method, the semantic relation between texts can be measured and recognized more accurately and effectively.

The present disclosure is directed to a tool that can be used to order, dispense, locate, request and administer medications as well as locate, issue and administer medical items and supplies for patients from a plurality of entry points into the system, e.g. handheld devices, mobile cart, etc. New workflows and functionality for various devices such as dispensing devices (automated dispensing cabinets) and issuing devices (e.g. open shelving) are also disclosed. Because of the rules governing abstracts, this abstract should not be used to construe the claims.

One embodiment of the present invention is an apparatus that processes XML, which apparatus comprises (a) an XML interface module that applies Document Type Definitions, XML Schema, XPath expressions and other XML model information to an XML model processor and applies XML character stream data to a parallel bit stream module, (b) an XML model processor that supplies symbol table entries to an XML symbol table module and regular expressions for validating XML data values to regular expression compiler, (c) an XML symbol table module that stores symbol table entries for later use in parsing, (d) a regular expression compiler that produces dynamic executable code for validating regular expressions using parallel bit streams, (e) a lexical item stream module that generates lexical items relevant to XML parsing and to validation of compiled regular expressions, (f) a transcoder that converts UTF-8 to UTF-16 as required, (g) a parser that makes parsing decisions in response to character streams in combination with lexical item streams and (h) a parsed data receiver to receive parsed data items from the parser.

A structured document processing apparatus includes an acquisition unit configured to acquire a structured document, a storage unit configured to store a structure model tree which indicates a typical structure of the acquired structured document, a parsing unit configured to parse the acquired structured document, an updating unit configured to update the structure model tree to match a structure of the parsed structured document therewith, a division unit configured to divide the acquired structured document into a plurality of lexical items, and a calculation unit configured to calculate frequency-of-occurrence information indicating locations of each of the lexical items in the acquired structured document.

The invention discloses an LDA (latent dirichlet allocation) and VSM (vector space model) based similar Chinese herb literature recommendation method. The method includes: adopting an IKAnalyzer to perform word segmentation on topics and summary information of literature on the basis of a terminological dictionary for Chinese herbs, constructing a vector space, performing dimensionality reduction on the vector space, constructing a semantic dictionary, numbering all lexical items in the dictionary in sequence, performing vectorization through each document on the basis of the semantic dictionary, constructing term vectors of each document, utilizing LDA and a Gibbs sampling algorithm to perform training to obtain probability distribution of each document on themes, then computing a value of similarity between every two documents by the aid of KL divergence, computing cosine similarity of the term vectors of each document on the basis of term frequency, performing joint weighting on the two kinds of similarities prior to performing similarity sorting, and then making recommendation. By the method, the literature, similar both in content and theme, in the Chinese herb literature can be recommended to users, and recommendation results are closer to user requirements.

The invention discloses a method for generating a retrieve lemma, a data retrieval method and a data retrieval system, wherein, the method for generating the retrieve lemma comprises the following steps: A. related vocabularies are inquired according to a lemma of an original subject, and a related vocabulary recommendation table is established; B. a database is inquired by utilization of the related vocabulary recommendation table, and a literature summary is retrieved; C. text mining of the retrieved literature summary is performed, and a recommended lemma which is matched with the content of a key word is obtained; D. the recommended lemma is inserted into the related vocabulary recommendation table to form a key word recommendation table. The method for generating the retrieve lemma, the data retrieval method and the data retrieval system mainly apply the automatic text mining technique and the statistical technique and combine a little manual correction to obtain the overall key word recommendation table and utilize the table for retrieval of the database.

The invention aims to provide a method and device for operating a corpus used for inputting contents. The method comprises the following steps of: acquiring a recommended content corresponding to user operation information according to the user operation information of one or more applications and a preset acquiring rule, and then updating the corpus according to the recommended content, thereby using the recommended content as a candidate lexical item used for inputting contents. Compared with the prior art, the method provided by the invention has the advantages that corresponding recommended words, pictures, specific characters, and the like are acquired according to the user operation information in each application and the preset acquiring rule, are updated into the corpus of the user and are used as the candidate lexical items used by the user for inputting the related contents. When the user carries out the inputting related to the operation after executing the operation, the user can quickly acquire the required lexical items from the candidate lexical items of an input method, thereby promoting the efficiency of inputting the contents of the user and promoting the user experience.

The invention discloses a user comment-based product search method. According to an information requirement provided by a user, a most related product list is searched by the method through combining product data and is returned to the user; the method comprises the following steps: analyzing the product data to obtain an index database, an affective characteristic database and a comment weight database; performing preprocessing and lexical item expansion on a query string submitted by the user to obtain a query lexical item set; searching products and obtaining final score values thereof; ordering from high to low according to the final score values of the products, and cutting off to obtain the product list. By adopting the method, the search effect can be optimized by the product comment information of the user; meanwhile the validity of the introduced information is ensured by analyzing the reference degree in a comment text; in addition, the production search application range and types queried by the user can be expanded; the method is suitable for the applications of product search, gift recommendation and the like of E-business websites.

The invention discloses a text keyword extracting method based on a subject model. The method comprises the following steps: firstly obtaining a probability matrix WT of the lexical item and the subject of a training text set between the lexical item and the subject obtained through training by the subject model from a great deal of text training sets by using the subject model method ; further obtaining a probability matrix B of the lexical item and the subject of candidate keyword composed of the set of probability vectors of the subject and the lexical item in a candidate keyword set A, and obtaining a word frequency weight vector D of the candidate keyword corresponding to the candidate keyword set, cyclically computing by using the probability matrix B of the subject of the candidate keyword through the weight vector of the lexical item of the candidate keyword and the subject vector of the text to obtain the finally modified text subject vector and lexical item weight proportion vector, and thus extracting the keyword of the text. According to the text keyword extracting method based on the subject model, the error in keyword extraction due to different lengths of texts is reduced, and the keyword more proper to represent the text content is extracted.

The invention provides a method and a device for generating a searching result. The method comprises the following steps of S1, using an anchor text of a webpage or a click text of a user in advance to obtain a lexical item of each website and the weight of each lexical item, and establishing the website model of each website; S2, acquiring a search term of the user, and obtaining each matched webpage matched with the search term through retrieval; S3, obtaining the domain relevance between the search term and the website corresponding to each matched webpage through correlation calculation by using the search term and the website model established in the step S1; and S4, according to the domain relevance between the search term and the website corresponding to each matched webpage, sequencing each matched webpage to generate the searching result. Compared with the prior art, the method has the advantages that the domain relevance sequencing of the searching result can be improved, the user is facilitated to quickly find the searching result, meanwhile, the efficiencies of the user and a system are improved, the interaction times are reduced, and the burden of a server is mitigated.

The invention discloses a short text classification method and apparatus. The method comprises the steps of performing word segmentation preprocessing on to-be-classified short texts and obtaining an extended word of each word obtained by word segmentation; obtaining weight values of each word and the extended word of each word according to a pre-constructed lexical item set; according to the weight values, obtaining a probability of each type that a short text belongs to by utilizing a plurality of SVM classification models; and determining the type that the short text belongs to according to a preset probability classification model. According to the short text classification method, the problem of short text characteristic sparsity is solved, the complexity due to the adoption of multiple classification models is effectively lowered, and actual application requirements are better met.

The invention belongs to the field of data mining and provides a method and system for advertisement recommendation based a microblog. The method comprises the steps that microblog data are read; the microblog data are initialized and a microblog text lexical item set is obtained; stop words of the microblog text lexical item set are deleted and a microblog text original feature lexical item set is obtained; mapping is conducted on the microblog text original feature lexical item set and a feature lexical item dictionary, whether lexical items in the microblog text original feature lexical item set exist in the feature lexical item dictionary or not is judged, and the tf-idf values of the appearing lexical items are calculated and serve as the feature values of the lexical items; whether the lexical items of the feature lexical item dictionary exist in the microblog text original feature lexical item set or not is judged and the feature values of the lexical items which do not appear are marked to be zero; feature vectors of the feature values obtained through calculation are automatically classified to classifications divided in advance; according to an automatic classification result, advertisements are recommended to a user. The advertisements recommended by the method and system are accurate and the effect is good.

The invention discloses a software defect positioning method based on text part of speech and a program call relation. The method comprises: (1) extracting text messages of summaries and descriptions in a defect report, and increasing weights of noun lexical items and weights of all lexical items of a summary module in the defect report according to part of speech tags; (2) filtering out elements not required by a source code file according to a demand parameter ran of a developer, and preprocessing the text messages of the defect report and the filtered source code file; (3) generating a suspicious defect source code file list; (4) finding out a called source file through character string retrieval, and increasing a similarity value to correct an original rank; and (5) outputting a defect source code file or a defect source code file list corresponding to the defect report according to the demand parameter ran of the developer. According to the software defect positioning method based on the text part of speech and the program call relation, the text part of speech is utilized to adjust the weights of the lexical items, the program call relation is utilized to correct the similarity value, and the source code file is filtered and a final result is output according to the demand of a programmer, so that the purpose of improving the accuracy of defect positioning is achieved.

The disclosed embodiments provide a method, system and apparatus for processing data. During operation, the system obtains a set of content items containing unstructured data. Next, the system obtains a set of part-of-speech (POS) tags for lexical items in the set of content items. The system then uses a computer to match the POS tags to one or more POS tagging patterns to obtain a set of candidate topics for the set of content items and extract a set of topics for the set of content items from the set of candidate topics.

The invention provides a geographic position searching method and a geographic position searching device. The establishing method comprises the following steps of acquiring a searching term inputted by a user; dividing the searching term so as to obtain various lexical items; retrieving one or a plurality of space data corresponding to the lexical items from a space database which is established in advance; determining an entity object of the searching term; sequentially performing space merging on the space data corresponding to each two adjacent lexical items; determining the geographic range of the entity object; positioning the geographic position of the entity object from the geographic range of the entity object; and returning to a searching result of the searching term. Compared with the prior art, the geographic position searching method has the advantages that even if the text modes of the space data do not have a leader-member relation, a correct searching result can be returned; the accuracy and the recall rate of geographic information searching can be increased; the searching demands of people are met; and the user experience is improved.

Systems and methods for semantic knowledge assessment, instruction, and acquisition are disclosed. In one embodiment a computer-implemented method for language instruction includes determining a lexical recognition ability level of a user within a lexicon of a particular language. This method further includes, based on item recognizability, creating a target list of unknown lexical items. The target list can be sorted by ranking the importance of the unknown lexical items within the particular lexicon. The method also includes generating a personal language learning sequence for the user based, at least in part, on the target list.

The invention provides a pseudo-correlation feedback model information retrieval method and system based on semantic similarity. The method comprises the following steps: carrying out a first query from a target document set according to a query keyword to extract a pseudo-related document set, carrying out query expansion by adopting a Rochio algorithm, carrying out query expansion according to the semantic similarity of sentences, fusing the results of the two query expansion methods, and carrying out a second query to realize final information retrieval. According to the invention, when theextended lexical item is selected; the importance degree relationship between the query lexical item and the extension word in the traditional method can be highlighted; the semantic correlation of the sentences where the lexical items are located is combined; the condition that lexical items are associated when sentence semantics are similar in reality is met; According to the method and the device, the conditions that the semantics are related even if the lexical items are different are represented, so that the query words have better regional indexing in a multi-semantic environment, a large amount of useless and irrelevant information can be removed from mass information, more accurate candidate words can be obtained, and the precision of expanded query and final retrieval can be improved.

The invention provides a named entity recognition-based news search result similarity calculation method, which comprises the following steps: establishing a plurality of key word subsets for a news search result by using a named entity recognition technology; establishing a lexical item matrix corresponding to each subset; calculating similarity in each lexical item matrix respectively; and finally, weighting a plurality of similarities to obtain a final similarity. According to the named entity recognition-based news search result similarity calculation method, the characteristic element of a piece of news is highlighted, the dimension of the lexical item matrixes can be effectively reduced, and the interaction among lexical items of different types is calculated during similarity calculation. The named entity recognition-based news search result similarity calculation method has the three characteristics of extracting a key word based on the named entity recognition, establishing a plurality of lexical items based on the key word subsets and calculating the weighting similarity based on the lexical item matrixes.