1313 results about "Semantic similarity" patented technology

A method of retrieving documents using a search engine includes providing a reverse index including one or more keywords and a list of documents containing the one or more keywords, the reverse index further including a measure of confidence (MOC) value associated with the one or more keywords. One or more query terms are input into the search engine. The query terms are disambiguated and a MOC value is associated with each meaning of the disambiguated query term. A list of documents is retrieved containing the query terms wherein the documents are initially ranked based at least in part on the MOC values of the keywords and query terms. The list of documents may be re-ranked based at least in part on the semantic similarity of each document to the disambiguated query terms.

The invention discloses a calculating method of text similarity degree and vocabulary meaning similarity degree and system and application system, which comprises the following steps: basing on vocabulary data bank; proceeding initialize; calculating; getting initial vocabulary meaning similarity degree among vocabulary in the vocabulary data bank; basing on the initial vocabulary meaning similarity degree; calculating initial semantic similarity degree among text; iterating semantic similarity degree among each text and vocabulary meaning similarity degree among vocabulary till constriction; constructuring final vocabulary meaning similar matrix with final vocabulary similarity degree; transforming the text vocabulary frequency vector of the initial text to the new text vocabulary text vocabulary frequency vector; calculating text similarity degree in the text collection. This invention can improve related property of current text especially about short text.

A method and apparatus for identifying coherent areas within a Web page. First, a Web page is parsed into an HTML DOM tree and an HTML tag token stream. Next, repeated-patterns are induced from the Web page. After filtering out improper repeated-patterns and generating corresponding instances of the repeated-patterns, the repeated-patterns are mapped back to corresponding regions in the Web page. Based on the mappings, a hierarchical RST tree containing information blocks is generated. Information items within the information blocks are detected then used to generate a hierarchical structural information block tree. Information blocks from the structural information block tree are then classified into text information blocks and link information blocks. Based on the classification and block semantic similarity, the bocks are clustered then grouped into semantic information blocks. The semantic information blocks contain main text information blocks and related link blocks which, if necessary, can be labeled.

A system and method for an efficient semantic similarity search of images with a classification structure are provided. The system and method provide for building a semantic classification-search tree for the plurality of images, the classification tree including at least two categories of images, each category of images representing a subset of the plurality of images, receiving a query image, classifying the query image to select one of the at least two categories of images, and restricting the search for the image of interest using the query image to the selected one of the at least two categories of images.

Provided is a process including: obtaining a clustered graph, the clustered graph having three or more clusters, each cluster having a plurality of nodes of the graph, the nodes being connected in pairs by one or more respective edges; determining visual attributes of cluster icons based on amounts of nodes in clusters corresponding to the respective cluster icons; determining positions of the cluster icons in a graphical visualization of the clustered graph; obtaining, for each cluster, a respective subset of nodes in the respective cluster; determining visual attributes of node icons based on attributes of corresponding nodes in the subsets of nodes, each node icon representing one of the nodes in the respective subset of nodes; determining positions of the node icons in the graphical visualization based on the positions of the corresponding cluster icons of clusters having the nodes corresponding to the respective node icons; and causing the graphical visualization to be displayed.

Methods and systems are provided for creating an adaptive thesaurus. A term pair including an index term and an expansion term is received. A recall gain, an expansion independence, and a semantic similarity of the term pair are calculated using a processor. Whether to store the term pair is determined based on the recall gain, the expansion independence, and the semantic similarity. The term pair is stored based on the determination. Methods and systems are provided for searching using an adaptive thesaurus. A search query including a query term is received. An expansion term stored in association with an index term matching the query term in the adaptive thesaurus is retrieved. Using a processor, the search query is expanded using the expansion term based on a recall gain, an expansion independence, and a semantic similarity.

The invention discloses a method for detecting code similarity based on semantic analysis of a program source code, which relates to computer program analyzing technology and a method for detecting complex codes of computer software. The method solves the prior problems of low similarity detection accuracy and high computing complexity on the codes of different syntactic representations and similar semantemes, and also solves the problem of incapability of realizing large-scale program code similarity detection. The method comprises the following steps: resolving two segments of source codes to be detected into two control dependence trees of a system dependence graph respectively and executing basic code standardization respectively; utilizing a measure method to extract candidate similar code control dependence trees of the control dependence trees which are subjected to the basic code standardization; executing an advanced code standardization operation on extracted candidate similar codes; and computing semantic similarity to obtain a similarity result so as to finish the code similarity detection. The method is applied to source code piracy detection, software component library query, software defect detection, program comprehension and the like.

Mechanisms for searching XML repositories for semantically related schemas from a variety of structured metadata sources, including web services, XSD documents and relational tables, in databases and Internet applications. A search is formulated as a problem of computing a maximum matching in pairwise bipartite graphs formed from query and repository schemas. The edges of such a bipartite graph capture the semantic similarity between corresponding attributes of the schema based on their name and type semantics. Tight upper and lower bounds are also derived on the maximum matching that can be used for fast ranking of matchings whilst still maintaining specified levels of precision and recall. Schema indexing is performed by ‘attribute hashing’, in which matching schemas of a database are found by indexing using query attributes, performing lower bound computations for maximum matching and recording peaks in the resulting histogram of hits.

The invention provides a problem cluster-based automatic asking and answering method and device. The method comprises the steps of clustering problems in an asking and answering database based on semantic similarity in advance to obtain more than one problem clusters, and determining fine quality answers corresponding to the problem clusters from answers of the problems in the problem clusters, thus forming a cluster-format asking and answering database; when the problem input by a user is obtained, determining the problem cluster with the highest semantic similarity with the problem input by the user in the cluster-format asking and answering database and returning the fine quality answer corresponding to the problem cluster to the user. According to the problem cluster-based automatic asking and answering method and device, efficient accurate automatic asking and answering can be realized aiming at the problem of the user and the user demands can be better met.

The application presents a question and answer recommendation method based on artificial intelligence, a question and answer recommendation device and a computer device. The question and answer recommendation method based on artificial intelligence includes steps of receiving search terms input by a user, wherein the search terms are questions; matching the search terms with questions having answers in a question and answer knowledge base; if the terms in question and answer knowledge base are not matched with the search terms completely, calculating the semantic similarity between the search terms and the questions having answers in the question and answer knowledge base; judging if the question and answer knowledge base has a question of which semantic similarity with the search terms is larger than a preset threshold value; if it does, using the answer of which semantic similarity with the search terms is more than the preset threshold value as the answer of the search term, and recommending to a user. Answer recommendation is realized according to the semantic similarity, thus the meaning transfer risk is reduced and the interference by meaningless oral expression element is reduced.

The invention belongs to the field of multimedia information retrieval and relates to a method for realizing thesaurus-based query expansion and sort in image retrieval. The method comprises a WordNet-based English word semantic similarity metric algorithm, a HowNet-based Chinese word semantic similarity metric algorithm, an expansion rule-based query expansion word selection and optimization algorithm and a retrieval result evaluation and optimization algorithm. In the method, an image search engine is improved by the relevant text processing method and the relevant semantic network dictionary; and the retrieval result is sorted through semantic expansion, user interaction and improved similarity measurement. Compared with the traditional method, the method has the advantages of high accuracy rate, high integrality and low space-time cost. The method has very important significance for performing high-efficiency image retrieval according to image high-layer semantic information and on the basis of a large-scale image data set, and has wide application value in the field of cross-linguistic and cross-media retrieval.

The invention provides a method for computing similarities among short texts. The method for computing the similarities among the short texts includes steps of 1), extracting features of the short texts; 2), matching the extracted features of the short texts with one another, and computing the semantic similarities of the short texts. The method has the advantages that semantic fuzziness and weights of terms in the texts are sufficiently considered, and accordingly effects of accurately seizing keywords of the texts can be realized.

The invention provides a telecommunication field package recommending method based on intelligent customer service robot interaction. The method comprises the following steps: a. acquiring a user interest model; and b. recommending individual demand-satisfied package service for a user by adopting a decision tree algorithm. The invention further provides an intelligent customer service robot system recommending engine device. Compared with the existing recommending method, the telecommunication field package recommending method is based on a scene interaction model in a recommending process, and can be used for carrying out similarity calculation according to similarity among the calculation labels, calculating the similarity among the labels by combining similarity between semantic similarity calculation with the traditional TF-IDF (Term Frequency-Inverse Document Frequency) in the similarity calculation, better reflecting the characteristics of users and resources by applying labels to show resource and user models, and improving the recommending quality.

Provided is a process including: obtaining a first graph comprising nodes and edges, each of the first-graph edges linking two of the first-graph nodes and denoting semantic similarity of unstructured text in documents corresponding to the two linked first-graph nodes; for each of the first-graph nodes, selecting nodes for a second graph from attributes of the unstructured text documents to which the first-graph node corresponds, wherein the attributes are entities mentioned in the unstructured text documents, and wherein each of the second-graph nodes corresponds to a respective selected attribute; and for each pair of the second-graph nodes, determining a respective edge weight indicating similarity between a first entity corresponding to a first node of the respective pair and a second entity corresponding to a second node of the respective pair.

The invention provides an intelligent response method, which comprises the following steps that: after a consultation question is preprocessed, constructing an inverted index for a question and answerknowledge base; through an inverted index query way, inquiring a candidate question set related to the consultation question from the question and answer knowledge base; aiming at each candidate question in the candidate question set; independently calculating a question similarity between the consultation question and the candidate question, wherein the question similarity is obtained through the linear weighting of a text similarity, a semantic similarity, a theme similarity and a syntax similarity between the consultation question and the corresponding candidate question; and finally, selecting a candidate question corresponding to the highest question similarity obtained by calculation, and inquiring the associated answer of the selected candidate question in the question and answer knowledge base as a target answer to be output. The invention also provides an electronic device and a storage medium. By use of the intelligent response method, the accuracy and the response efficiency of intelligent response can be improved, and service quality is improved.

Determining semantically equivalent text or questions using hybrid representations based on neural network learning. Weighted bag-of-words and convolutional neural networks (CNN) based distributed vector representations of questions or text may be generated to compute the semantic similarity between questions or text. Weighted bag-of-words and CNN based distributed vector representations may be jointly used to compute the semantic similarity. A pair-wise ranking loss function trains neural network. In one embodiment, the parameters of the system are trained by minimizing a pair-wise ranking loss function over a training set using stochastic gradient descent (SGD).

A system and method for composing application services includes an indexing module configured to index words in a request and available application descriptions to create a semantic similarity map. A semantic matcher is configured to determine semantic similarity between concepts/terms in both domain-independent and domain-specific ontologies for the semantic similarity map. A prefiltering module is configured to determine candidate compositions for the request based on the semantic similarity map and the available descriptions. A metric guided composition method is configured to run algorithms to generate a set of alternative compositions by determining which applications can be composed with which others using the semantic similarity map.

The invention discloses a theme word vector and network structure-based theme keyword extraction method, and particularly relates to the technical field of extracting keywords from texts. The theme word vector and network structure-based theme keyword extraction method comprises the following steps of: carrying out theme clustering on a text corpus on the basis of an LDA theme model, and obtaining100 keywords, relevancies of which with each theme are top 100 in the theme; expressing each word in the text corpus as a word vector by utilizing word2vec, obtaining a semantic similarity between every two words through calculation, and respectively calculating 5 words, semantic similarities of which with each keyword in the keywords are top 5, wherein the keywords and the words, the semantic similarities of which with each keyword are top 5 form a new keyword set; and constructing a keyword network and obtaining the top 20 words in each set to serve as keywords of the theme. According to the method, keywords which have relatively high word frequencies in documents can be extracted, and keywords which have relatively word frequencies and are strongly associated with themes can be effectively discovered.

A confidence scoring system can include a model trained using features extracted from translations that have received user translation ratings. The features can include, e.g. sentence length, an amount of out-of-vocabulary or rare words, language model probability scores of the source or translation, or a semantic similarity between the source and a translation. Parameters of the confidence model can then be adjusted based on a comparison of the confidence model output and user translation ratings, where the user translation ratings can be selected or weighted based on a determination of individual user fluentness. After the confidence model has been trained, it can produce confidence scores for new translations. If a confidence score is higher than a threshold, it can indicate the translation should be selected for automatic presentation to users. If the confidence score is below another threshold, it can indicate the translation should be updated.

The invention provides a text similarity measuring system based on multi-feature fusion and relates to the field of intelligent information processing. According to the system, the text similarity is measured by fusing multiple features based on word frequencies, word vectors and Wikipedia labels. The invention aims to solve the problem of semantic loss caused by non-considering of contexts in a conventional text similarity measuring system and the problem of low similarity result accuracy caused by larger text length difference. The text similarity measuring system is implemented by the following steps: carrying out preprocessing such as word segmentation and stop word removal on a training text; training corpora of the processed training text as a word vector model; measuring the similarity based on the word frequencies, the similarity based on the word vectors and the similarity based on the Wikipedia labels between input text pairs to be computed, and carrying out weighted summation to obtain a final text semantic similarity measuring result. According to the system, the measurement accuracy of the text similarities can be improved, so that the requirement on intelligent information processing is met.

The embodiment of the invention provides a video bullet screen filtering method and device. One specific embodiment of the method comprises the following steps: acquiring a current video bullet screen text to be pushed and target users; judging whether the target users already set filtering conditions or not; when the target users already set the filtering conditions, performing semantic analysis on the current video bullet screen text to be pushed through a preset semantic analysis method, and determining a semantic frame of the current video bullet screen text; calculating semantic similarities between the semantic frame of the current video bullet screen text and semantic frames for filtering according to the semantic frame of the current video bullet screen text and at least one semantic frame for filtering; and determining whether to filter the current video bullet screen or not for the target users on the basis of the semantic similarities. Through the embodiment, user operations can be simplified, and the video bullet screen filtering efficiency is increased.

The present invention discloses a statistics-based machine translation method and apparatus and an electronic device, a semantic similarity-degree calculation method and apparatus and an electronic device, and a word quantization method and apparatus and an electronic device. The statistics-based machine translation method comprises: according to a feature that affects a translation probability and that is of each candidate translation and a pre-generated translation probability prediction model generating a translation probability of a sentence to be translated into each candidate translation, wherein the feature that affects the translation probability at least comprises a semantic similarity-degree between the sentence to be translated and the candidate translation; and selecting a preset number of candidate translations whose translation probabilities rank top as a translation of the sentence to be translated. By adoption of the statistics-based machine translation method provided by the present application, the semantic level of the natural language can be reached deeply when the machine translation model is constructed, and the deviation of semantics between the translation and the source text is avoided, so as to achieve the effect of improving translation quality.

A recommendation system exploits a repository of mashups to provide design-time assistance to the user through relevant suggestions as to what outputs can be generated along with the best plans to generate those outputs. An output ranker ranks the outputs of the system base on their popularity scores, and a planner uses metric planning algorithms and a configurable utility function. The system takes into account popularity and semantic similarity when recommending services and sources.