Abstract: A text containing a word is received by a computing device. The word is compared to inventory words in a sense inventory. The sense inventory comprises at least one inventory word and at least one concept corresponding to the at least one inventory word. Upon matching the word to an inventory word in the sense inventory, a concept for the word is identified by comparing each concept related to the inventory word to the word. The concept is assigned the word.

Abstract: A method and system for facilitating a semantic search based on one or more corpuses of natural language texts are provided. One or more corpuses of natural language texts are received including indexed linguistic parameters and semantic structures of lexical units. The linguistic parameters and semantic structures are generated during a preliminary syntactico-semantic analysis. Searching for text fragments satisfying a query in the one or more corpuses is performed. Relevance of the search results is estimated.

Abstract: A system for natural language processing is provided. A first natural language processing program may be constructed using language-independent semantic descriptions, and language-dependent morphological descriptions, lexical descriptions, and syntactic descriptions of one or more target languages. The natural language processing program may include any of machine translation, fact extraction, semantic indexing, semantic search, sentiment analysis, document classification, summarization, big data analysis, or another program. Additional sets of natural language processing programs may be constructed.

Abstract: Systems and methods for creating ontologies by analyzing natural language texts. An example method comprises: receiving a plurality of semantic structures associated with a text corpus; identifying a first semantic structure and a second semantic structure, wherein the first semantic structure comprises a first substructure and a second substructure, wherein the second semantic structure comprises a third substructure and a fourth substructure, and wherein the first substructure is similar to the third substructure in view of a first similarity criterion; and responsive to determining that the second substructure is similar to the fourth substructure in view of a second similarity criterion, associating, with a certain concept of an ontology associated with the text corpus, objects represented by the second substructure and the fourth substructure.

Abstract: The present disclosure provides methods and systems for performing syntactic analysis of a text. In some implementations the method includes performing rough syntactic analysis of the text, generating a graph of generalized constituents of the text and filtering arcs of the graph of generalized constituents with a combination classifier which includes a tree classifier and one or more linear classifiers. The combination classifier is trained using parallel analysis of an untagged two-language text corpus.

Abstract: Systems and methods for task distribution are provided. A total number of available computing system's processing units is defined, where the total number of available processing units includes a set of regular processing units available for executing tasks and a set of processing units that constitute the reserve pool. Tasks are assigned to processing units. The number of processing units assigned to the next task in the queue is no more than the total number of processing units available at the time, multiplied by the availability ratio. Iterative assignment of processing units to tasks according to the method described is performed as long as there are idle processing units available for task execution, when no more processing units are available, the processing units from the reserve pool are assigned. As a result, the method allows processing units to be available for allocation to a new incoming task at any time.

Abstract: Disclosed are systems, computer-readable mediums, and methods for extracting named entities from an untagged corpus of texts. Generating a set of attributes for each of the tokens based at least on a deep semantic-syntactic analysis. The set of attributes include lexical, syntactic, and semantic attributes. Selecting a subset of the attributes for each of the tokens. Retrieving classifier attributes and categories based on a trained model, wherein the classifier attributes are related to one or more categories. Comparing the subset of the attributes for each of the tokens with the classifier attributes. Classifying each of tokens to at least one of the categories based on the comparing. Generating tagged text based on the categorized tokens.

Abstract: Methods are described for performing classification (categorization) of text documents written in various languages. Language-independent semantic structures are constructed before classifying documents. These structures reflect lexical, morphological, syntactic, and semantic properties of documents. The methods suggested are able to perform cross-language text classification which is based on document properties reflecting their meaning. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, etc.

Abstract: A method, system, and computer program product for generating and using a user ontological model for natural language processing of user-provided text, including receiving definitions of user ontological objects and generating user ontological models. A semantic-syntactic tree generated from user-provided text is analyzed. Information objects based on the user ontological objects are generated by the analysis.

Abstract: Disclosed are methods, systems, and computer-readable mediums for automatic training of a syntactic and semantic parser using a genetic algorithm. An initial population is created, where the initial population comprises a vector of parameters for elements of syntactic and semantic descriptions of a source sentence. A natural language compiler (NLC) system is used to translate the sentence from the source language into a target language based on the syntactic and semantic descriptions of the source sentence. A vector of quality ratings is generated where each quality rating in the vector of quality ratings is of a corresponding parameter in the vector of parameters. Quality ratings are evaluated according to specific criterion, which comprise parameters such as a BLEU score and a number of emergency sentences. A number of parameters in the vector of parameters are replaced with adjusted parameters.

Abstract: Methods are described for performing clustering or classification of texts of different languages. Language-independent semantic structures (LISS) are constructed before clustering is performed. These structures reflect lexical, morphological, syntactic, and semantic properties of texts. The methods suggested are able to perform cross-language text clustering which is based on the meaning derived from texts. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, internet searches, and creating corpora for other tasks, etc.

Abstract: A method and computer system for analyzing sentences of various languages and constructing a language-independent semantic structure are provided. On the basis of comprehensive knowledge about languages and semantics, exhaustive linguistic descriptions are created, and lexical, morphological, syntactic, and semantic analyses for one or more sentences of a natural or artificial language are performed. A computer system is also provided to implement, analyze and store various linguistic structures and to perform lexical, morphological, syntactic, and semantic analyses. As result, a generalized data structure, such as a semantic structure, is generated and used to describe the meaning of one or more sentences in language-independent form, applicable to automated abstracting, machine translation, control systems, Internet information retrieval, etc.

Abstract: In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.

Abstract: Disclosed is a method for translating text fragments displayed on a screen from an input language into an output language and displaying the result. Translation may use electronic dictionaries, machine translation, natural language processing, control systems, information searches, (e.g., search engine via an Internet protocol), semantic searches, computer-aided learning, and expert systems. For a word combination, appropriate local or network accessible dictionaries are consulted. The disclosed method provides a translation in grammatical agreement in accordance with grammatical rules of the output language in consideration of the context of the text.

Abstract: A method and computer system for analyzing a text corpus in a natural language is provided. An initial morphological description having word inflection rules for various groups of words in the natural language is created by a linguist. A plurality of text corpuses are analyzed to obtain information on the occurrence of a plurality of word forms for each word token in each text corpus. A morphological dictionary which contains information about each base form and word inflection rules for each word token with verified hypothesis is generated.

Abstract: Described herein are methods for finding substantially similar/different sources (files and documents), and estimating similarity or difference between given sources. Similarity and difference may be found across a variety of formats. Sources may be in one or more languages such that similarity and difference may be found across any number and types of languages. A variety of characteristics may be used to arrive at an overall measure of similarity or difference including determining or identifying syntactic roles, semantic roles and semantic classes in reference to sources.