188 results about "Lexical item" patented technology

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 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.

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 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 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 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 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 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 discloses an automatic short text semantic concept expansion method based on an open knowledge base, which comprises the steps that elements in n-gram sets generated by short texts are linked to concepts most relevant to the elements in the open knowledge base, and expandable semantic concept sets are generated for the elements based on a concept relationship matrix and the linked concepts of the open knowledge base. According to the method, only anchor text information in a document of the open knowledge base, rather than lexical item information and directory information of the document, is adopted to construct the concept relationship matrix, so that the construction and calculation of the matrix are convenient, and the problems of low granularity ratio of the directory information and many different meanings are solved. During a semantic concept expansion stage, a context based semantic similarity calculation method is adopted for semantic concept expansion, and context consistency of a short text content and similarity of the concepts in an abstract semantic layer are considered, so that the semantic concept expansion accuracy is improved.

The invention discloses a financial public opinion perception method based on a weighted LDA (latent Dirichlet allocation) topic model and belongs to the technical field of natural language understanding and processing as well as network public opinion. Everyday financial public opinions are perceived on the basis of microblog data related to everyday finance and are quantified according to 'everyday financial public opinion comprehensive index'. The 'everyday financial public opinion comprehensive index' is a weighted average of all financial related blog emotion values on the day, and the blog emotion values are a result of text emotion classification of blog content. An SVM (support vector machine) classification model based on weighted LDA is adopted for text emotion classification and adopts the weighted LDA for establishing text represented hidden topic space, objective data indirectly embodying investor sentiment and subjective data directly embodying investor sentiment are organically combined with a new lexical item weight calculation method, and accurate understanding of texts from the semantic level is promoted greatly, so that the text emotion classification effect is better.