743 results about "Text document" patented technology

A method of encoding knowledge is disclosed, which can be used to automatically detect problems in software application deployments. The method includes accessing a source of knowledge describing a problem known to occur in deployments of a particular software application, and which identifies a plurality of conditions associated with the problem. An encoded representation of the knowledge source is generated according to a predefined knowledge encoding methodology. The encoded representation is adapted to be applied automatically by a computer to analyze data representing a current state of a monitored deployment of the software application to detect whether the conditions and the problem exist therein. In various implementations, the encoded representation of the knowledge can include queries for deployment information, information concerning the relative importance of the conditions to a detection of the problem, and/or logical constructs for computing a confidence value in the existence of the problem and for determining whether to report the problem if some of the conditions are not true. The knowledge source can comprise a text document (such as a knowledge base article), a flowchart of a diagnostic troubleshooting method, and the like. Also disclosed are methods of at least partially automating the encoding process.

In the present invention, a textual document is syntactically analyzed and knowledge is constructed from a single word or plural words. The knowledge is then marked, from the broken down knowledge (represented by the underscores in FIG. 1) or from a part-of-speech, as a related object (node) or a relation (edge) (represented by ‘n’ or ‘e’ shown in FIG. 1). In other words, in the present invention a textual document is treated as knowledge constructed from a single word or plural words. The knowledge extracted from the textual document is structured to form a knowledge structure (such as a graph structure constituted from nodes and edges). At least one link can be established between each of the knowledge structure elements and a semantically closest concept entry in a hierarchical concept dictionary.

A Natural Language Understanding system is provided for indexing of free text documents. The system according to the invention utilizes typographical and functional segmentation of text to identify those portions of free text that carry meaning. The system then uses words and multi-word terms and phrases identified in the free to text to identify concepts in the free text. The system uses a lexicon of terms linked to a formal ontology that is independent of a specific language to extract concepts from the free text based on the words and multi-word terms in the free text. The formal ontology contains both language independent domain knowledge concepts and language dependent linguistic concepts that govern the relationships between concepts and contain the rules about how language works. The system according to the current invention may preferably be used to index medical documents and assign codes from independent coding systems, such as, SNOMED, ICD-9 and ICD-10. The system according to the current invention may also preferably make use of syntactic parsing to improve the efficiency of the method.

Systems and methods interactive document search, retrieval, categorization, and summarization are provided. A document organizer processor may analyze the content of documents, such as web pages and text documents, downloaded from a computer network, such as the Internet or an intranet, in response to a user's search query. After receiving a search query from a user, the processor may locate documents related to the query, parse words in the documents into a word set, filter out unnecessary words, group the documents into categories, provide labels for the categories, construct summaries of the documents in each category, determine if any additional words or phases are to be recommended, present the labels and summaries to the user, and enable the user to iteratively refine the search.

A mechanism is provided for cross-linking information sources using multiple modalities. Text documents, images, audio sources, video, and other media are analyzed to determine media descriptors, which are metadata describing the content of the media sources. The media descriptors from all modalities are collated and cross-linked. A query processing and presentation module, which receives queries and presents results, may also be provided. A query may consist of textual keywords from user input. Alternatively, a query may derive from a media source, such as a text document, image, audio source, or video source.

A system and method of testing and tuning a speech recognition system by providing pronunciations to the speech recognizer. First a text document is provided to the system and converted into a sequence of phonemes representative of the words in the text. The phonemes are then converted to model units, such as Hidden Markov Models. From the models a probability is obtained for each model or state, and feature vectors are determined. The feature vector matching the most probable vector for each state is selected for each model. These ideal feature vectors are provided to the speech recognizer, and processed. The end result is compared with the original text, and modifications to the system can be made based on the output text.

A method and system for extracting opinions about a subject of interest from a text document in which each sentence is analyzed individually to identify the opinions. The most relevant feature terms related to the subject are extracted from the document based on their relevancy scores. Candidate feature terms are definite noun phrases at the beginning of the sentences. For each sentence that refers to the subject or a feature term, the invention determines whether the sentence includes an opinion polarity about the subject or the feature term. The opinion polarity is detected by identifying opinion terms in the sentence using an opinion dictionary or an opinion rule base, parsing the sentence with an English parser to identify grammatical components in the sentence and their relationships, and finding a matching entry in the dictionary or the rule base.

Systems, methods and computer readable media for performing a domain-specific metasearch, and obtaining search results therefrom. A metasearch engine capable of accessing generic, web-based search engines and domain-relevant search engines is provided to receive one or more queries inputted by a user, and to search for documents on at least one the generic, web-based search engines and domain-relevant search engines which are relevant to the queries. Raw data search results are fetched in the form of text documents. Relevant data including semantic information are extracted from the raw data search results, and converted to a local format. The relevant data having been converted to the local format may be visualized as a network visualization. Additionally or alternatively, the raw data search results may be ranked and/or filtered based on the linking of the relevant data. Visualization of the raw data having been ranked and/or filtered may be performed in addition to, or alternative to visualization of the network.

Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.

A method and apparatus are disclosed for transmitting advertising information to a mobile terminal over a first connection during a synchronization session and storing the advertising information in a local memory of the mobile terminal. A rich text document is transmitted to the mobile terminal over a second connection, wherein the rich text document comprises conditional code comprising advertising display criteria including a first identifier for retrieving selected advertising information stored in the local memory of the mobile terminal and a second identifier for downloading the selected advertising information from the Internet.

To facilitate the use of audio files for annotation purposes, an audio file format, which includes audio data for playback purposes, is augmented with a parallel data channel of line identifiers, or with a map associating time codes for the audio data with line numbers on the original document. The line number-time code information in the audio file is used to navigate within the audio file, and also to associate bookmark links and captured audio annotation files with line numbers of the original text document. An annotation device may provide an output document wherein links to audio and/or text annotation files are embedded at corresponding line numbers. Also, a navigation index may be generated, having links to annotation files and associated document line numbers, as well as bookmark links to selected document line numbers.

Coding free text documents, especially in medicine, has become an urgent priority as electronic medical records (EMR) mature, and the need to exchange data between EMRs becomes more acute. However, only a few automated coding systems exist, and they can only code a small portion of the free text against a limited number of codes. The precision of these systems is low and code quality is not measured. The present invention discloses a process and system which implements semantic coding against standard lexicon(s) with high precision. The standard lexicon can come from a number of different sources, but is usually developed by a standard's body. The system is semi-automated to enable medical coders or others to process free text documents at a rapid rate and with high precision. The system performs the steps of segmenting a document, flagging the need for corrections, validating the document against a data type definition, and looking up both the semantics and standard codes which correspond to the document's sentences. The coder has the option to intervene at any step in the process to fix mistakes made by the system. A knowledge base, consisting of propositions, represents the semantic knowledge in the domain. When sentences with unknown semantics are discovered they can be easily added to the knowledge base. The propositions in the knowledge base are associated with codes in the standard lexicon. The quality of each match is rated by a professional who understands the knowledge domain. The system uses this information to perform high precision coding and measure the quality of the match.

A method designed to include a number of features which facilitate fast and accurate reading of text documents. The design of these features is based on research in the fields of visual perception, ergonomics, and usability. The method applies this research to create software which provides various assisted-reading modes.

A method and system for automated knowledge extraction and organization, which uses information retrieval services to identify text documents related to a specific topic, to identify and extract trends and patterns from the identified documents, and to transform those trends and patterns into an understandable, useful and organized information resource. An information extraction engine extracts concepts and associated text passages from the identified text documents. A clustering engine organizes the most significant concepts in a hierarchical taxonomy. A hypertext knowledge base generator generates a knowledge base by organizing the extracted concepts and associated text passages according to the hierarchical taxonomy.

A method and apparatus is disclosed for making predictions about entities represented in documents and for information analysis of text documents or the like, from a large number of such documents. Predictive models are executed responsive to variables derived from canonical documents to determine documents containing desired attributes or characteristics. The canonical documents are derived from standardized documents, which, in turn, are derived from original documents.

A method for sentiment classification of a text document using high-order n-grams utilizes a multilevel embedding strategy to project n-grams into a low-dimensional latent semantic space where the projection parameters are trained in a supervised fashion together with the sentiment classification task. Using, for example, a deep convolutional neural network, the semantic embedding of n-grams, the bag-of-occurrence representation of text from n-grams, and the classification function from each review to the sentiment class are learned jointly in one unified discriminative framework.

A system and method of visually summarizing a hierarchically structured set of digital objects and for facilitating efficient access to such objects through the selection of representative summary objects. These digital objects are typically media objects such as digital image files, digital video clips, digital audio objects, such as “MP3” files, or other digital documents, such as text documents, that can be collected by a user and distributed over a variety of storage media and storage locations.

A credentials verification network utilizes blockchain technology to track/verify credentials for candidates that can later be used in the hiring verification process. As implemented by nodes of a distributed network, a candidate receives from a first network node a secure credential generated by a credentialing source from an original credential document. The first node processes the secure credential according to a security feature (e.g., hash function) to create a transaction, and assigns an identifier to the transaction. The first node signs the transaction with a private key to create a transaction signature. The first node sends the signed transaction to a central server that broadcasts the transaction to the blockchain network. Once the network has accepted the transaction, the candidate will be notified and handed over the raw text document and transaction receipt ID. The candidate can forward the information to a recruiter or potential employer running a second node.

The invention relates to computer science, information-search and intelligent systems, and can be used in developing information-search and other information and intelligent systems that operate on the basis of Internet. The invention provides the possibility of automatic creation of knowledge by extraction of knowledge from textual documents in electronic form in different languages; intelligent processing of textual information and users' requests to extract knowledge in any foreign language. The claimed method provides a mechanism of self-learning in the form of a stochastically indexed system of artifical intelligence, providing automatic instruction of the system in rules of grammatical and semantic analysis. The method includes creating databases of stochastically indexed dictionaries, tables of indices of linguistic texts and knowledge bases of morphological analysis; performing morphological and syntactical analysis, and also stochastic indexing of textual documents in respect to a given theme from the search system in a given language, and creating knowledge base of syntactical analysis. Stochastically indexed textual documents pertaining to the given theme are subjected to semantic analysis, and knowledge bases of semantic analysis. A user's request is compiled and transformed, in the stochastically indexed form, into a plurality of new requests that are equivalent to the original request; and stochastically indexed fragments of textual documents that comprise all word combinations of the transformed request are selected. A stochastically indexed structure is generated from the selected documents and basing on said structure by means of logical conclusion a brief reply of the system is generated. Relevancy of the obtained brief reply is checked by generating an interrogative sentence based on said reply, and by comparing said sentence with the request. When the user's request is identical to the obtained interrogative sentence, the decision is made that the brief reply of the system is identical to the request, and the reply is submitted to the user.