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19613results about "Semantic analysis" patented technology

Method and system for automatically extracting relations between concepts included in text

A method and system for automatically extracting relations between concepts included in electronic text is described. Aspects the exemplary embodiment include a semantic network comprising a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets. The semantic network further includes semantic information comprising at least one of: 1) an expanded set of semantic relation links representing: hierarchical semantic relations, synset / corpus semantic relations verb / subject semantic relations, verb / direct object semantic relations, and fine grain / coarse grain semantic relationship; 2) a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and 3) a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language. A linguistic engine uses the semantic network to performing semantic disambiguation on the electronic text using one or more of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference.
Owner:EXPERT AI SPA

Apparatus and methods for developing conversational applications

Apparatus with accompanying subsystems and methods for developing conversational computer applications. As a user interface, the apparatus allows for a user to initiate the conversation. The apparatus also answers simple and complex questions, understands complex requests, pursues the user for further information when the request is incomplete, and in general provides customer support with a human like conversation while, at the same time, it is capable to interact with a company's proprietary database. As a development tool, the apparatus allows a software developer to implement a conversational system much faster than takes, with current commercial systems to implement basic dialog flows. The apparatus contains three major subsystems: a state transition inference engine, a heuristic answer engine and a parser generator with semantic augmentations. A main process broker controls the flow and the interaction between the different subsystems. The state transition inference engine handles requests that require processing a transaction or retrieving exact information. The heuristic answer engine answers questions that do not require exact answers but enough information to fulfill the user's request. The parser generator processes the user's natural language request, that is, it processes the syntactical structure of the natural language requests and it builds a conceptual structure of the request. After the parser generator processes the user's request, a main process broker feeds the conceptual structure to either the heuristic answer engine or to the state transition inference engine. The interaction between the main process broker and the subsystems creates a conversational environment between the user and the apparatus, while the apparatus uses information from proprietary databases to provide information, or process information, during the course of the conversation. The apparatus is equipped with a programming interface that allows implementers to declare and specify transactions based requests and answers to a multiplicity of questions. The apparatus may be used with a speech recognition interface, in which case, the apparatus improves the recognition results through the context implicitly created by the apparatus.
Owner:GYRUS LOGIC INC

Method and system for the automatic recognition of deceptive language

A system for identifying deception within a text includes a processor for receiving and processing a text file. The processor includes a deception indicator tag analyzer for inserting into the text file at least one deception indicator tag that identifies a potentially deceptive word or phrase within the text file, and an interpreter for interpreting the at least one deception indicator tag to determine a distribution of potentially deceptive word or phrases within the text file and generating deception likelihood data based upon the density or distribution of potentially deceptive word or phrases within the text file. A method for identifying deception within a text includes the steps of receiving a first text to be analyzed, normalizing the first text to produce a normalized text, inserting into the normalized text at least one part-of-speech tag that identifies a part of speech of a word associated with the part-of-speech tag, inserting into the normalized text at least one syntactic label that identifies a linguistic construction of one or more words associated with the syntactic label, inserting into the normalized text at least one deception indicator tag that identifies a potentially deceptive word or phrase within the normalized text, interpreting the at least one deception indicator tag to determine a distribution of potentially deceptive word or phrases within the normalized text, and generating deception likelihood data based upon the density or frequency of distribution of potentially deceptive word or phrases within the normalized text.
Owner:DECEPTION DISCOVERY TECH

Natural language processor

A computer program product for controlling the computer's processor to perform responsive actions a natural language input has: (1) vocabulary, phrase and concept databases of words, phrase and concepts, respectively, that can be recognized in the inputted communication, wherein each of these database elements is representable by a designated semantic symbol, (2) means for searching the inputted communication to identify the words in the communication that are contained within the vocabulary database, (3) means for expressing the communication in terms of the word semantic symbols that correspond to each of the words identified in the inputted communication, (4) means for searching the communication when expressed in terms of its corresponding word semantic symbols so as to identify the phrases in the communication that are contained within the phrase database, (5) means for expressing the communication in terms of the phrase semantic symbols that correspond to each of the phrases identified in the communication, (6) means for searching the communication when expressed in terms of its corresponding phrase semantic symbols so as to identify the concepts in the communication that are contained within the concept database, and (7) means for expressing the communication in terms of the concept semantic symbols that correspond to each of the concepts identified in the inputted communication, wherein these concept semantic symbols are recognizable by the processor and can cause the processor to take action responsive to the inputted communication.
Owner:SONUM TECH
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