84 results about "Web page categorization" patented technology

The invention discloses a system for automatic classification analysis for websites based on website contents. The system comprises a capture module, a website text content analysis module, a word segmentation module, a feature training extracting module and a website classification module. The feature training extracting module selects a plurality of features words with maximum weights by calculating importance degree, distinction degree and feature keyword weight of every candidate feature word and sorting the candidate feature words according to the feature keyword weights, wherein the feature keyword weights are used as weightings after the normalization of the selected feature words and a website classification vector template is created according to the given sets of the selected feature words and the feature keyword weights. The website classification module is used for generating a feature spatial vector according to the given set of the selected feature words and the weightings which are obtained by the feature training extracting module and identifying the classification of a website by calculating the similarity between the feature spatial vector and the feature spatial vector of the website. The system is capable of effectively solving the problem of network information in a mess and allowing users to searching information for positioning conveniently and accurately.

The invention relates to a web page automatic classification method based on a training set. A classification process is the combination of methods of characteristic selection, characteristic weight value determination, text vector comparison, and the like. The automatic classification method based on a classification system mainly classes a document to be classified into a corresponding sort according to a beforehand established sort model, namely a training set. Along with the development of the multimedia technique, the content forms of web page information are also rich and colorful, and contents not only comprise text information but also comprise much structural information and other form information, such as sound, figures, images, and the like. However, because web pages based on texts still possess larger proportions, the classification based on web page texts still takes the precedence. The method has reliable theoretical support and favorable extensibility and accuracy and is easy to be in butt joint with application interfaces correlative to an operator.

An intelligent web page classification device based on the user's behaviors: (1) Perform background input with an initial classification sample group for training, so as to gain a clustering center of each classification in the characteristic space. (2) Receive a URL input by the user input before catching and analyzing corresponding pages on the background; then, output texts with index value in the page. Moreover, extract a characteristic set according to the user-input content and web page contents; then, perform feedback modification for the characteristic space in the initial classification sample group and adjust the characteristic weight factor in the vector space. (3) Use the user-selected classification device to perform automatic classification for the texts in the previous step of the created texts and output results. When the user executes a search, the classification device can automatically determine the classification of each result and perform gradual adjustment for the classification device; the more times the user executes the search, the more accurate the classification of the web page classification device will be, so as to help different users effectively reduce the size of the set of the search result before locating necessary information more accurately.

The invention relates to a method assisted for searching network information. Through the method, diversified expansion of a keyword can be realized; the method can be embedded into a plurality of web information service systems; the method is based on a simple webpage preprocessing and organizing mechanism; the method is capable of obtaining a diversified expansion word set in different ranges of the keywords, building an index network based on webpage classification according to a hyperlink of an internet and realizing diversified expansion of the keyword based on the built index network; and even if a user did not inquire the keyword or the field before, the expansion still can recommend most possible query semantics of the user.

The invention relates to the technical field of Internet, in particular to a webpage classification method for semi-supervised multi-view learning which comprises the following steps of: obtaining data from a webpage, and establishing a training set; training a classifier through the marked training set; encoding the marked training set and the unmarked training set through a trained classifier toobtain sample features; performing density clustering on the sample features to obtain a clustering result; and classifying the samples of the unmarked training set according to a clustering result.According to the scheme, the marked training set is used for training the classifier; orthogonal constraints and adversarial similarity constraints are added on the basis of an existing multi-view classification method, density clustering marking is carried out on all data in a training set through a trained classifier, finally, accuracy verification is carried out on the classifier, and the classification performance of the classifier can be improved through multiple iterations of the process.

The invention discloses web page text classification algorithm research based on web page link analysis and a support vector machine and relates to the technical field of web page classification. The method includes the specific steps that 1, a large number of web pages are divided into a training set and a test set; 2, the web pages (including the training set and the test set) are preprocessed; 3, the word frequencies of feature words in each web page in the training set are calculated; 4, the weights of the feature words in each web page in the training set are calculated; 5, feature vectors of each class in the test set are calculated; 6, text feature vectors of each web page in the training set are calculated; 7, the minimum similarity value is determined as the threshold value; 8, the number of the feature words is reduced to the maximum degree; 9, text feature vectors of the web pages in the test set are classified; 10, the similarity between the classified web pages and the feature vectors is calculated and tested at the same time. A method in which a space vector model and the support vector machine is adopted is used, and the web page text classification algorithm research has the advantages of being short in classification time, high in recall rate, low in memory requirement and high in learning rate.

The invention provides a web page classification method based on deep learning with fusion of text and structural features. Firstly, a HTML (HyperText Markup Language) document of the web page is obtained by a crawler, and the key text information such as title, meta, hyperlink and so on is extracted, and the text vocabulary is converted into vector (word2vec) to represent the text features. Thenthe HTML tags are traversed and transformed into vectors to represent the structural characteristics of the web page. Finally, the vector is input into the long-term and short-term memory network (LSTM), and the heterogeneous web page text features and web page structure features are fused into the training model through the neural network to classify. This method synthesizes the distinguishing features to represent the web pages more comprehensively and improves the classification accuracy.

The invention discloses a student browsed webpage classification method based on N-Gram and a naive Bayesian classifier. The method comprises the specific implementation steps that first, URL description information is crawled from a navigation website, four classification corpora are constructed, corpus texts are expressed in the forms of uni-gram and bi-gram, TF-IDF is used as a weight of text characteristics, and a naive Bayesian classification algorithm is used to construct the classifier; and URLs in student browsed records are segmented according to set rules, URL categories are determined through matching of the classifier and a URL category base, and if the URL categories determined through the classifier conform to set confidence, the URL categories are added into the URL category base. Through the method, the URLs in the student browsed records are effectively classified, and therefore the webpage recognition rate and the classification accuracy rate are increased.

The invention provides an inquiry word classification method that includes: acquiring user-input inquiry words; recording web pages that the user click with the inquiry word; acquiring the classification information of the web page; and determining the inquiry word classification results according to the classification parameters. The classification parameters include web page classification information. Through classification for web page classification corresponding to inquiry words, the invention can determine the classification of the inquiry words, so as to improve the efficiency of the inquiry word classification and save resources. Moreover, the invention further provides an apparatus for inquiry word classification as well as a search engine system comprises the apparatus.

The invention discloses webpage classification method and device. The method includes establishing a virtual hierarchical URL (uniform resource locator) according to recording in an existing URL class library, and predicting the class of the hierarchical URL; when classification on webpages to be classified is needed, searching the URL class library according to URLs of the webpages to be classified; if matching URLs are unfound, searching the URL class library according to higher-level URLs of the URLs; and when matching URLs are found, determining the classes of the webpages to be classified according to predicted classes of the found URLs. Efficiency and success rate in webpage classification by the method and device are improved.

The invention relates to a webpage type recognition method and a webpage type recognition device. The webpage type recognition method includes the steps of receiving a webpage address of a webpage to be tested, analyzing the webpage address to obtain a constituent part of the webpage address, judging whether the constituent part of the address of the webpage to be tested is matched with a webpage classification rule or not, classifying the webpage to be tested according to the webpage classification rule to obtain the webpage type of the webpage to be tested if the constituent part of the address of the webpage to be tested is matched with the webpage classification rule, and sending the webpage address of the webpage to be tested to a webpage classification device to be classified to obtain the webpage type of the webpage to be tested if the constituent part of the address of the webpage to be tested is not matched with the webpage classification rule. According to the webpage type recognition method and the webpage type recognition device, under the condition that only the webpage address is used, the webpage type can be forecast, the forecast speed is high, and the instantaneity is high.

The invention discloses a webpage classifying method. The method includes the steps of obtaining N-dimensional current features of a to-be-classified webpage, wherein N is a positive integer; inputting the N-dimensional current features in a trained stacking model for feature expansion to obtain (N+n)-dimensional features of the to-be-classified webpage, wherein the stacking model is a model formed by conducting q-layer stacking on p basic classifying models, n is the product of p and q, and n, p and q are positive integers; obtaining a classifying result of the to-be-classified webpage through a classifying algorithm according to the (N+n)-dimensional features. By means of the method, the N-dimensional current features of the to-be-classified webpage are expanded through the stacking model, and the webpage classifying accuracy is improved on the premise of not depending on a search engine or third-party service. The invention further discloses a webpage classifying system, a webpage classifying device and a computer readable storage medium. The same technical effects can be achieved.

The invention provides a web page classification method, a web page classification device and network equipment. The method comprises the following steps of: extracting information of different classification weight levels in a source file of a web page; performing word segmentation processing on information of each classification weight level to acquire segmented words of each classification weight level; and performing classification processing on the web page by using the segmented words of each classification weight level according to a sequence of the classification weight level from high to low. According to the technical scheme provided by the invention, classification processing is performed on the web page by preferably using the information with higher classification weight level by using the characteristic that the more important information in the web page has higher influence on a web page classification result, so that the influence of invalid information on web page classification in the web page is favorably reduced, and further the accuracy of web page classification is favorably improved.

The invention aims at providing a webpage classification method and a webpage classification system, wherein the method comprises the following steps that: a webpage-to-be-classified obtaining device receives a domain name input by a user, and obtains an URL (Uniform Resource Locator) corresponding to a webpage of breadcrumb to be crawled on the basis of the domain name; a breadcrumb crawling device crawls the breadcrumb of the webpage on the basis of the URL; and a webpage classifier classifies the webpage on the basis of the crawled breadcrumb. Compared with the prior art, the method and the system have the advantages that the breadcrumb is extracted from the webpage on the basis of the domain name; the webpage is classified; and the webpage classification accuracy is effectively improved.

The invention provides a method for auditing a webpage based on a cloud semantic database, which is mainly applied to the fields of an online information security, an online behavior management and the like. The invention uses a cloud technology and a semantic analysis technology to construct the cloud semantic database; the semantic analysis and word frequency statistics are carried out on the online webpage contents of a user captured by an online behavior management system, and online webpage contents are matched with the cloud semantic database to obtain webpage classification; and the cloud semantic database is revised in accordance with feedback audit results.

The invention discloses an online semantic excavation system of Chinese polysemic words and based on a uniform resource locator (URL). The system utilizes a webpage classification method based on the URL and can conduct semantic excavation on the Chinese polysemic words online. The process includes first constructing a URL classifier through an online URL classification catalogue; then classifying searching results (including webpage URLs and abstracts) of the polysemic words returned by a search engine by means of the URL classifier to obtain initial semantic classification results of the polysemic words; finally clustering the initial semantic classification results according to the webpage abstracts to obtain semantic excavation results of the polysemic words. The semantic excavation system has ideal accuracy and recall rate and is highly applicable to semantic excavation of network popular words.

The invention discloses an interest point searching method and system for a mobile internet. The method comprises the following steps of: acquiring HTTP logs of users accessed to the mobile internet from a DPI system; extracting a user search URL according to a searching characteristic library; determining user search interest points by adopting a keyword classification method; if successful, ending the process; and if not, determining the user search interest points by adopting a webpage classification method. According to the interest point searching method and system for the mobile internet disclosed by the invention, starting with a basic network, two analysis methods are combined; mobile internet search interest points of user can be analyzed more accurately; the accuracy rate and the coverage rate for analysis of the mobile internet search interest points can be increased; and powerful data support is provided for product marketing and improving the user experience.

A method of performing transactional web page searches is disclosed. The method includes examining a plurality of web pages, identifying transactional features within a set of the plurality of web pages, and classifying the set of web pages as transactional. The method proceeds with annotating and indexing the transactional web pages, and, in response to a user-designated transactional query, providing only the set of web pages that have been classified as transactional. The identifying transactional features comprises checking for the existence of positive patterns and verifying the absence of negative patterns with respect to a set of contents within each of the plurality of web pages and comprises identifying transactional actions to be performed and identifying transactional objects of the transactional actions to be performed. The annotating and indexing the transactional features comprises annotating and indexing transactional actions and transactional objects.

The invention discloses a web page classification method based on a von Mises-Fisher probability model, and belongs to the technical field of the Internet and machine learning. The method comprises the following steps: at first, carrying out data preprocessing, feature extraction and feature screening on a training sample, modeling, and then, substituting a feature vector to a web page to be classified in the model to realize final classification. The web page classification method disclosed by the invention is used for carrying out two-norm normalization on the obtained feature vector to prepare for modeling the von Mises-Fisher model while eliminating the influence of a text length on the feature vector; and the von Mises-Fisher probability model is used for modeling the text feature vector, and the model is applied to the field of natural language processing for the first time.