321 results about "Vector space model" patented technology

Methods and apparatuses to assign part-of-speech tags to words are described. An input sequence of words is received. A global fabric of a corpus having training sequences of words may be analyzed in a vector space. A global semantic information associated with the input sequence of words may be extracted based on the analyzing. A part-of-speech tag may be assigned to a word of the input sequence based on POS tags from pertinent words in relevant training sequences identified using the global semantic information. The input sequence may be mapped into a vector space. A neighborhood associated with the input sequence may be formed in the vector space wherein the neighborhood represents one or more training sequences that are globally relevant to the input sequence.

A system and method for browsing, retrieving, and recommending information from a collection uses multi-modal features of the documents in the collection, as well as an analysis of users' prior browsing and retrieval behavior. The system and method are premised on various disclosed methods for quantitatively representing documents in a document collection as vectors in multi-dimensional vector spaces, quantitatively determining similarity between documents, and clustering documents according to those similarities. The system and method also rely on methods for quantitatively representing users in a user population, quantitatively determining similarity between users, clustering users according to those similarities, and visually representing clusters of users by analogy to clusters of documents.

A system, method, and software product provide for unsupervised identification of complex, nonlinear subspaces in high dimensional data. The system includes a vector quantization module, a weighted topology representing graph module, and an encoding module. The vector quantization module takes vector data inputs and extracts a group of inputs about a number of cluster centers, using a globally optimized clustering process. The weighted topology representing graph module creates a weighted graph of the vector space, using the cluster centers as nodes, weighting edges between nodes as a function of the density of the vectors between the linked nodes. The encoding module uses the weighted graph to recode the input vectors based on their proximity to the cluster centers and the connectedness of the graph. The recoded vectors are reinput into the vector quantization module, and the process repeated until termination, for example at a limited number of cluster centers. Upon termination, the clusters thus identified may be highly nonlinear in the original data space.

The subject innovation provides for systems and methods to facilitate weighted suffix tree clustering. Conventional suffix tree cluster models can be augmented by incorporating quality measures to facilitate improved performance. Further the quality measure can be employed in determining cluster labels that show improvements in accuracy over conventional means. Additionally “stopnodes” can be defined to facilitate traversing suffix tree models efficiently. Quality measurements can be determined based in part on weighting factors applied to terms in a vector model, said terms being mapped from a suffix tree model.

A method based on Chinese website punctuation triple recognition and text content filtering. The method based on existing URL, the website information keywords in the method of filtration - filtration rate and the low rate of filtration of the whole problem, Bringing on a method for composite based on the URL and on keywords, as well as text-based knowledge representation method of vector space website text content filtering. Applying to a method Based on black-and-white list of URL filtering and Chinese punctuation statistical characteristics to effectively remove navigation information, relevant linked information, advertising linked information, copyright information and other Web content noise information to extract content of text; adopting vector space model text knowledge representation, By calculating vector text template and unhealthy information in the feature vector cosine angle, and set the threshold, compared to the text of the class. The invention can be widely used in the filtering of undesirable information network and website personalized information services.

A vehicle environment monitoring system is provided that is based on a three-dimensional vector model. The three-dimensional vector model of the vehicle's environment is generated on the basis of the image data captured by at least one three-dimensional camera. Out of the image data, particular data are extracted for generating the three-dimensional vector model in order to reduce the data volume. For data extraction, a data extraction algorithm is applied that is determined in accordance with at least one parameter that relates to the situation of the vehicle. Therefore, targeted data extraction is performed for generating a three-dimensional model that is particularly adapted for an application that is desired in the current vehicle situation. The applications of the vector model include driver assistance, external monitoring and vehicle control, as well as recording in an event data recorder. In one implementation, a sequence of three-dimensional vector models, representing a three-dimensional space-and-time model, is generated.

The invention discloses a method for mining data in construction regulation field based on associative regulation mining technology; 1. a construction regulation text vector space model is generated, 2. a construction regulation data vector space model is generated, 3. the construction regulation data vector space model is subject transposition to generate a construction regulation data feature vector space model, namely, a frequent feature set is generated, and 4. construction regulation data association degree is calculated and an association rule is output. The method can mine the data in construction regulation field, provides higher recall ratio for a user inquiring data, recommends associative query contents, and solves the technical problem that the existing association analysis technologies can not carry out association analysis on outlier data.

The invention discloses a network hot event detection method based on text classification and clustering analysis. The method solves the problem that the efficiency and accuracy rate of the existing network hot event detection method based on clustering analysis need to be improved. The method comprises the steps that feature words are respectively selected for various classes of files through feature extraction and feature selection by utilizing a training corpus; each training text and test text are represented as vectors in all of the feature spaces by utilizing a vector space model method, and the weight of each dimension of the vectors is determined by utilizing a TF-IDF (term frequency-inverse document frequency) method, and then each test text is classified; the classified test texts in different classes are respectively subjected to clustering analysis, so the hot cluster of each class is obtained, the feature word representing the hot event is obtained through further analysis, and then the word property and other aspects of each feature word are analyzed; the description of each hot event is generated by utilizing relevant language knowledge and necessary linguistic organization. With the network hot event detection method based on text classification and clustering analysis, the detection efficiency and accuracy rate of hot events can be effectively improved.

The invention discloses a multi-model fused short text classification method. The multi-model fused short text classification method comprises a learning method and a classification method. The learning method comprises the following steps: carrying out word segmentation and filtration on short text training data to obtain a word set; calculating the IDF value of each word; calculating the TFIDF values of all the words and constructing a text vector VSM; and carrying out text learning on the basis of a vector space model, and constructing an ontology tree model, a keyword overlapping model, a naive Bayesian model and a support vector machine model. The classification method comprises the following steps: carrying out word segmentation and filtration on a to-be-classified short text; generating a text vector on the basis of the support vector machine model; respectively classifying by using the ontology tree model, the keyword overlapping model, the naive Bayesian model and the support vector machine model to obtain single model classification results; and fusing the single model classification results to obtain a final classification result. According to the method disclosed in the invention, multiple classification modes are fused and the short text classification correctness is improved.

The invention belongs to the technical field of multi-document summarization and provides a multi-document summarization method based on text segmentation, which comprises the following steps of: using HowNet to obtain a concept, building a concept vector space model, conducting text segmentation by adopting an improved DotPlotting model and a sentence concept vector space, calculating sentence weight by using the built concept vector space model, generating a summary according to the sentence weight, the text segmentation and the similarity situation, and evaluating the generated summary by using the ROUGE-N evaluation method and using F_Score as an evaluation index. According to the result, the multi-document summarization by using a text segmentation technique is effective, relevant documents provided by users can be gathered to form a summary by adopting the multi-document summarization method, the summary is displayed to the users in a proper way, the information acquisition efficiency is greatly improved, the practicability is high and the popularization and application values are greater.

The invention relates to a quick multi-keyword semantic sorting search method for protecting data privacy in cloud computing. A domain weighted scoring concept is introduced in document scoring, and keywords in different domains such as a title, an abstract and the like are endowed with different weights to be distinguished; a retrieval keyword is subjected to semantic expansion, semantic similarity is calculated, a three-factor sorting method is designed by combining the semantic similarity, the domain weighted scoring and a correlation score, and a cloud server can perform accurate sorting on search results and return a sorting result to a search user; and for the defect of low query efficiency of a searchable encryption scheme, a vector block segmentation marking matching algorithm is designed, and a document vector created by a vector space model is subjected to block segmentation to generate a marking vector with a relatively small dimension number. According to the method, the query efficiency can be improved, the index creation time can be shortened, and semantic ciphertext keyword search is realized.

An extension of the vector space model for computing chemical similarity using textual and chemical descriptors is described. The method uses a chemical and/or textual description of a molecule/chemical and a decomposes a molecule/chemical descriptor matrix by a suitable technique such as singular value decomposition to create a low dimensional representation of the original descriptor space. Similarities between a user probe and the textual and/or chemical descriptors are then computed and ranked.

The invention discloses a text similarity matching method on the basis of a vector space model. The text similarity matching method includes extracting keywords of texts, clustering all the keywords and generating a keyword concept tree; and computing the similarity of the texts according to the created keyword concept tree of the keywords in the texts to be translated, and acquiring texts in a translation depository according to the similarity. The texts in the translation depository are matched with the texts to be translated. According to the technical scheme, the test similarity matching method has the advantages that relations among the texts can be relatively accurately reflected, so that the similarity of the texts can be sufficiently reflected.

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 relates to a topic information acquisition method based on network topology. An initial web page set is obtained from a search engine and is expressed as a vector set through purification, word division and removal of stop words, and a vector space model is used to calculate the text similarity. A network structure is utilized to perform linkage analysis to extracted URLs first, the linkage is filtered through directory hierarchies of the URLs, and then the weights of the URLs are modified according to the scaleless property of a network to perform the prior absorption selection. At the same time, unrelated topic areas are feedback, and the lengths of buffer areas of unrelated URLs are set through the distance between the URLs and a seed set. The heat of acquired topics is calculated to select one topic to obtain a new reply.

The invention discloses a natural language intention understanding method in man-machine interaction.The method comprises the steps that intention labeling is conducted on text natural language instruction data, and each sentence of text is labeled with an intention; the text is vectorized, on the basis of a traditional text vector space model, information of parts of speech of a text instruction is fused, and a new text representation model, namely, a vector space model of the parts of speech is defined; a stackable denoising auto-encoder is applied to natural language instruction intention understanding, and the high-order characteristic of the instruction is extracted; at last, training and prediction are conducted through a support vector machine, and intention understanding of the natural language instruction is achieved.According to the natural language intention understanding method in man-machine interaction, more semantic information in the natural language instruction can be excavated, the recognition rate of intention understanding is increased, the stackable denoising auto-encoder is adopted, random noise is added during the training process, the actual application scene is more approached, and a model obtained from training has higher generalization capacity.

The invention discloses an information recommendation method and system combining image content and keywords. The information recommendation method combining image content and keywords comprises the steps that keyword information of images in an image library and image content information containing color features and textural features are extracted, the keyword information and the image content information are expressed as a vector space model, and a corresponding keyword information matrix and an image content information matrix are obtained; the keyword information matrix and the image content information matrix are processed by utilizing a linear sparse model, and a similarity chart is obtained by calculating the similarity among the images; an image similar to a target image is inquired from the similarity chart according to the target image searched by a user, and an original recommendation list is formed; the original recommendation list is arranged to obtain a final recommendation list, and the final recommendation list is displayed.

The invention discloses a job recommending method, and belongs to the technical field of recommending systems. The job recommending method has the advantages that the Matthew effect is avoided, the problem of cold start is solved, and the populations are well utilized to realize personalized recommending. The job recommending method comprises the following steps of obtaining user data and job data; establishing a user preference vector space model and a job vector space model; according to the user preference vector space model and the job vector space model, calculating multi-domain scoring values based on contents, obtaining first scoring values of jobs, and sequencing, so as to obtain a job set; when one job is submitted and belongs to the job set, calculating the scoring valves of the corresponding job based on the similarity of user background information according to the user preference vector space model and the job data, and obtaining second scoring valves of the corresponding job; according to the first scoring valves and the second scoring valves, obtaining the mixed scoring valves of the corresponding job, and sequencing, so as to obtain a recommending list.

The invention relates to an implementation method for directionally running internet advertisements, comprising the following steps: step S110 of clustering all target websites according to a theme at first, calculating similarity among web pages with a vector space model according to a clustering algorithm, and clustering web pages according to similarity; step S120 of marking a theme for each class of websites, then judging and counting a crowd attribute for web pages under each theme, wherein the crowd attribute comprises gender, age, income, identity, education, interest and family status; step S130 of analyzing to-be-run advertisements, wherein analyzed contents comprise judgment for adjustment type and analysis for crowd attribute of run advertisement; step S140 of matching web page advertisements according to the crowd attribute analyzed by advertisements and web pages, and running advertisements on corresponding web pages. The implementation method for directionally running internet advertisements provided by the invention is capable of increasing accuracy and is characterized by good practical applicability.