Abbyy Infopoisk LLC

Abbyy Software Limited

Abbyy Production LLC

Vladimir Pavlovich Selegey

Konstantin Zuev

Konstantin Anisimovich

Diar Tuganbaev

Tatiana Danielyan

Tatiana Parfentieva

Alexey Maramchin

Konstantin Anismovich

Anatoly Starostin

Dmitry Konstantinovich Mesheryakov

Anton Krivenko

Evgeny Shavlyugin

Alexey Marachim

Disclosed are systems, computer-readable mediums, and methods for detecting language ambiguity. A sentence is analyzed to determine syntactic relationships among generalized constituents of the sentence. A graph of the generalized constituents is formed based on syntactic relationships and a lexical-morphological structure of the sentence. The graph is analyzed to determine a plurality of syntactic structures of the sentence. Each syntactic structure is rated on its probability that the syntactic structure is an accurate hypothesis about a full syntactic structure of the sentence. Semantic structures corresponding to the syntactic structures are determined. A first semantic structure and a second semantic structure of the semantic structures are selected, where the first and second semantic structures are different and each have a corresponding syntactic structure having a rating of at least a threshold value. A semantic ambiguity in the sentence is determined based on a difference between the first and second semantic structures.

Language ambiguity detection of text

A method and computer system for translating sentences between languages from an intermediate language-independent semantic representation is provided. On the basis of comprehensive understanding about languages and semantics, exhaustive linguistic descriptions are used to analyze sentences, to build syntactic structures and language independent semantic structures and representations, and to synthesize one or more sentences in a natural or artificial language. A computer system is also provided to analyze and synthesize various linguistic structures and to perform translation of a wide spectrum of various sentence types. As result, a generalized data structure, such as a semantic structure, is generated from a sentence of an input language and can be transformed into a natural sentence expressing its meaning correctly in an output language. The method and computer system can be applied to in automated abstracting, machine translation, natural language processing, control systems, Internet information retrieval, etc.

Method and system for translating sentence between languages based on semantic structure of the sentence

The preferred embodiments provide an automated machine translation from one language to another. The source language may contain expressions or words that are not readily handled by the translation system. Such problematic words or word combinations may, for example, include the words not found in the dictionary of the translation system, as well as text fragments corresponding to structures with low ratings. To improve translation quality, such potentially erroneous words or questionable word combinations are identified by the translation system and displayed to a user by distinctive display styles in the display of a document in the source language and in its translation to a target language. A user is provided with a capability to correct erroneous or questionable words so as to improve the quality of translation.

Indicating and correcting errors in machine translation systems

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.

Exhaustive automatic processing of textual information

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.

Cross-language text classification

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.

Method and system for analyzing and translating various languages with use of semantic hierarchy

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.

Deep model statistics method for machine translation

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.

Translation of a selected text fragment of a screen

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.

Method and system for generating new entries in natural language dictionary

Methods and computer systems for translating sentences between languages from an intermediate language-independent semantic representation are provided. Based on a comprehensive understanding about languages and semantics, exhaustive linguistic descriptions are used to analyze sentences, build syntactic structures and language independent semantic structures and representations, and synthesize one or more sentences in a natural or artificial language. A computer system is also provided to analyze and synthesize various linguistic structures and perform translation of a wide spectrum of various sentence types. As result, a generalized data structure, such as a semantic structure, is generated from a sentence of an input language and can be transformed into a natural sentence expressing its meaning correctly in an output language. The methods and systems can be applied to automated abstracting, machine translation, natural language processing, control systems, Internet information retrieval, etc.

Translating texts between languages

Computer-implemented systems and methods align fragments of a first text with corresponding fragments of a second text, which is a translation of the first text. One preferred embodiment preliminarily divides the first and second texts into fragments; generates a hypothesis about the correspondence between the fragments of the first and second texts; performs a lexico-morphological analysis of the fragments using linguistic descriptions; performs a syntactic analysis of the fragments using linguistic descriptions and generates syntactic structures for the fragments; generates semantic structures for the fragments; and estimates the degree of correspondence between the semantic structures.

Methods and systems for alignment of parallel text corpora

Embodiments of the present invention disclose computer-implemented method for generating examples for electronic dictionaries to serve as an aid to translation between languages, comprising: for at least one dictionary entry comprising a headword Win a source language and at least one translation T, T, . . . Tfor the headword Win a target language: generating a first set comprising possible forms for the headword Win the source language and a second set comprising possible forms for each translation T, T, . . . Tin the target language; searching a corpus of translations for at least one translation sentence pair that includes the headword W, or one of its generated forms, in a first part of the pair, and a translation Tor one of its generated forms, in a second part of the pair; and providing each translation sentence pair to a user.

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Moscow, Russia

Vladimir Pavlovich Selegey