1227 results about "Keyword extraction" patented technology

A mobile device and method are provided. The mobile device includes an input unit that receives a user voice command, a keyword extracting unit that extracts at least one keyword from the user voice command, a search unit that searches Electronic Program Guide (EPG) data for broadcast information related to the at least one keyword, and a generating unit that generates a broadcast control signal corresponding to the broadcast information related to the at least one keyword.

A translation direction specifying unit specifies a first language and a second language. A speech recognizing unit recognizes a speech signal of the first language and outputs a first language character string. A first translating unit translates the first language character string into a second language character string that will be displayed on a display device. A keyword extracting unit extracts a keyword for a document retrieval from the first language character string or the second language character string, with which a document retrieving unit performs a document retrieval. A second translating unit translates a retrieved document into its opponent language, which will be displayed on the display device.

The present invention provides a method of processing at least one natural language text using a graph. The method includes determining a plurality of text units based upon the natural language text, associating the plurality of text units with a plurality of graph nodes, and determining at least one connecting relation between at least two of the plurality of text units. The method also includes associating the at least one connecting relation with at least one graph edge connecting at least two of the plurality of graph nodes and determining a plurality of rankings associated with the plurality of graph nodes based upon the at least one graph edge. The method can also include a graphical visualization of at least one important text unit in a natural language text or collection of texts. Methods for word sense disambiguation, keyword extraction, and sentence extraction are also provided.

A computer-implemented system and method for keyword extraction are disclosed. The system in an example embodiment includes a keyword extraction component to extract relevant keywords from content of a web page, to identify items relevant to the extracted keywords, and to rank the relevant items.

A computer-implemented system and method for keyword extraction and contextual advertisement generation are disclosed. The system in an example embodiment includes a keyword extraction service to obtain information related to user activity on a host site and to extract relevant keywords from content of a web page, the information related to user activity on the host site being used to determine relevancy of the extracted keywords, and a contextual advertiser to produce an advertisement placement on an affiliate web page, the produced advertisement placement being relevant to user activity on the host site.

A computer-implemented system and method for keyword extraction and contextual advertisement generation are disclosed. The system in an example embodiment includes a keyword extraction service to receive from a consumer application a request for activation of a keyword extraction service via an application programming interface, the request including an identity of a content source, the request further including an identification of a particular extraction process to be used by the keyword extraction service on the identified content source; determine if the keyword extraction service has already processed the identified content source and retained extracted keywords in a data store; extract keywords from the identified content source using the particular extraction process identified in the request; and make the extracted keywords accessible to the consumer application.

A method to extract new keywords based on word frequency and multiple grammars is provided, which belongs to the technology field of a natural language processing, and is characterized by extracting the potential models of part of speech of the multiple grammars of the keywords by researching characteristic part of speech of the keywords and adopting computer to assist excavation and taking the models as the basis of the keywords to extract arithmetic. When extracting the new keywords, firstly excavating the multiple phrases in text in accordance with the potential models of part of speech and extract candidate word set of the keywords, and then excavating potential keywords not loading from titles and add the potential keywords to the candidate keyword set. The application brings forward an improved single text word frequency/inverse text frequency value (tf/idf) format, introduces target-oriented characteristics, grades the candidate keywords, obtains the order of the candidate keywords and gives the keywords of news document after optimizing the results. Compared with the traditional keyword extraction method based on single text word frequency/inverse text frequency value (tf/idf), the method has higher recall rate under the condition of the same precision.

A method for implementing a question answering system based on a question-answer pair comprises the following steps: question analysis, question retrieval and answer selection. After a user submits aquestion expressed in natural language to the question answering system, the question answering system uses question vectorization, keyword extraction, keyword extension and other natural language processing techniques to understand the questioning intent of a user, and then uses the engine searching method in the question-answer pair database to obtain the question-related candidate question-answer pair set, and uses a matching algorithm and sorting algorithm to accurately select the best answer from the candidate sets. The invention obtains the function of the matching degree score between the question and the answer by learning by synthesizing different algorithms and models, the method of choosing the best answer from the candidate question-answer pairs is realized, and an answer selection method based on convolution neural network and Xgboost feature fusion is completed, which provides a better method for the answer selection of the question answering system.

The invention relates to a text label extracting method. The text label extracting method comprises the following steps: category prediction is performed on a to-be-extracted text through a text categorization model, and a target category of the text is obtained; topic prediction is performed on the to-be-extracted text through a topic clustering model, and a predicted topic is obtained; if the predicted topic is in a default topic set, a target topic corresponding to the predicted topic is acquired, keyword extraction is performed on the to-be-extracted text, target keywords of the text are obtained, and the target category, the target topic and the target keywords are taken as labels of the text. The text labels have different levels to meet multi-granularity retrieval requirements, and multi-granularity recommended articles can be provided according to different labels. Besides, the invention provides a text label extracting device.

An information communication terminal (100) that includes: a speech recognition module (6) for recognizing speech information to identify a plurality of words in the recognized speech information; a storage medium (20) for storing keyword extraction condition setting data (24) in which a condition for extracting a keyword is set; a keyword extraction module (8) for reading the keyword extraction condition setting data (24) to extract a plurality of keywords from the plurality of words; a related information acquisition module (11) for acquiring related information related to a plurality of keywords; and a related information output module (14) for providing related information to a monitor (2).

A system and method are described that enable users to navigate on the web according to use's own keyword definition on web site, keyword extraction and processing from user's visiting website, user's selection on keyword categories, and mapping between E-mail address and URL. The user's own keyword definition on web site is user-driven keyword naming scheme is opposite) method of the keyword domain services which were service company-driven method. The user's selection on keyword categories provides users choice on keyword categories and group. The keyword extraction and processing from user's visiting web site provides keyword extraction from the page and arranges for related keywords in order to prepare for anticipated search and navigation from the user's current web site and keyword. The mapping system between E-mail and URL provides conversion of E-mail address into URL, in order to use as domain name.

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.

Embodiments of the invention provide a keyword extraction method and apparatus. The method comprises the steps of performing word segmentation on a text by utilizing a word segmentation device to obtain words and filtering the words to obtain candidate keywords; calculating the similarity between any two candidate keywords; according to the similarity, calculating a weight of each candidate keyword, and according to a preset corpus, calculating an inverse document frequency of the candidate keyword; and according to the weight and the inverse document frequency of the candidate keyword, obtaining a key degree of the candidate keyword, and according to the key degree of the candidate keyword, selecting a keyword. Therefore, the accuracy of keyword extraction is improved.

A keyword analysis device obtains word vectors represented by the documents by analyzing keywords contained in each of documents input in a designated period. A topic cluster extraction device extracts topic clusters belonging to the same topic from a plurality of documents. A keyword extraction device extracts, as a characteristic keyword group, a predetermined number of keywords from the topic cluster in descending order of appearance frequency. A topic structurization determination device determines whether the topic can be structurized, by segmenting the topic cluster into subtopic clusters with reference to the number of documents, the variance of dates contained in the documents, or the C-value of keyword contained in the documents, as a determination criterion. And a keyword presentation device presents the characteristic keyword group in the subtopic cluster upon arranging the keyword group on the basis of the date information.

The object of the present invention is to obtain a keyword extracting device which extracts keywords collectively and efficiently while improving descriptive property and reusability of the information for keyword extracting. A keyword extracting device of the present invention comprises text data input means for inputting a text, pattern processing means for carrying out matching and replacement of a character string based on a pattern in regular expression or its equivalent, pattern storage means having at least a keyword component pattern representing a character string capable of being a component of a keyword, keyword component extracting means for extracting, as keyword components, all character strings which are matched with a keyword component pattern and are not overlapped with each other by using the pattern processing means for a text, keyword candidate set generating means for generating a keyword candidate set from each keyword component, and keyword output means for outputting each keyword candidate of a keyword candidate set as a keyword.

The invention discloses a deep learning-based text keyword extraction method. The method comprises the following steps of: firstly training a recurrent neural network model, wherein the used training data comprise a large amount of texts and keywords thereof, and the training target is maximizing text-based condition probability of the keywords; converting each text and the keyword thereof into word vectors, inputting the word vectors into the recurrent neural network model and updating network parameters by using a random gradient descent method; and after the model training is finished, converting a section of text, the keyword of which is to be extracted, into a word vector, inputting the word vector into the trained recurrent neural network model so as to generate the keyword of the section of text. According to the method disclosed by the invention, the extraction of text keywords is realized by learning an end-to-end model through data driving; and compared with the traditional statistics and linguistics-based method, the method disclosed by the invention is stronger in adaptability, and can be used for obtaining different models according to different training data so as to extract keywords according to the requirements of specific fields.

The invention discloses a topic feature text keyword extraction method. Through the method, text keyword extraction results better than those of a traditional TF-IDF method can be obtained. Accordingto the technical scheme, at a training stage, word segmentation, stop word removal, part-of-speech filtering and other preprocessing are performed on a training text, statistical analysis is performedon inverse document frequency of words, meanwhile a topic model method is utilized to learn and obtain a topic probability matrix of the words, normalization processing is performed, topic distribution entropy of the words is calculated according to the topic probability matrix of the words, global weights of the words are calculated in combination with the inverse document frequency and the topic distribution entropy, and global weight calculation results are output to a test stage; and after a test text is preprocessed, statistical analysis is performed on normalized term frequency of wordsin the test text, the normalized term frequency is combined with the global weight calculation results obtained at the training stage, comprehensive scores of the words are calculated are ordered, and a plurality of words with the highest scores in the score order are used as automatic keyword extraction results of the current test text.

A system and method for recommending an application by using a keyword. A keyword-based application recommending user terminal includes a keyword extraction unit for extracting a keyword from an application that is running, a recommended application determination unit for determining an application to be recommended to correspond to the extracted keyword and a display unit for displaying the determined application.