annotation data generation assisting system includes: an input/output device receiving an input through an interactive process; morphological analysis system and dependency parsing system performing morphological and dependency parsing on text data in text archive; first to fourth candidate generating units detecting a zero anaphor or a referring expression in the dependency relation of a predicate in a sequence of morphemes, identifying a position as an object of annotation and estimating candidates of expressions to be inserted by using language knowledge; a candidate DB storing estimated candidates; and an interactive annotation device reading candidates of annotation from candidate DB and annotate a candidate selected by an interactive process by input/output device.

The present invention discloses a Tibetan character constituent analysis method, a Tibetan sorting method and corresponding devices, and relates to the field of natural language processing. The present invention is proposed to solve the problem that the existing Tibetan sorting methods have no universality or compatibility, which is inconvenient for the use of automatic computer Tibetan sorting. The technical solution provided by the present invention includes: S10, acquiring a Tibetan text to be analyzed; S20, using Tibetan characters in the Tibetan text as the input of a preset finite state automaton group; and S30, acquiring the constituents of the Tibetan characters according to a target finite state automaton, when the target finite state automaton in the finite state automaton group determines that the Tibetan characters in the Tibetan text are correctly spelled.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

A method for suggesting revisions to a document-under-analysis from a seed database, the seed database including a plurality of original texts each respectively associated with one of a plurality of final texts, the method for suggesting revisions including selecting a statement-under-analysis (“SUA”), selecting a first original text of the plurality of original texts, determining a first edit-type classification of the first original text with respect to its associated final text, generating a first similarity score for the first original text based on the first edit-type classification, the first similarity score representing a degree of similarity between the SUA and the first original text, selecting a second original text of the plurality of original texts, determining a second edit-type classification of the second original text with respect to its associated final text, generating a second similarity score for the second original text based on the second edit-type classification, the second similarity score representing a degree of similarity between the SUA and the second original text, selecting a candidate original text from one of the first original text and the second original text, and creating an edited SUA (“ESUA”) by modifying a copy of the first SUA consistent with a first candidate final text associated with the first candidate original text.

Computer-implemented method of browsing a resource through voice interaction comprising the following steps: A. acquiring (100) from a user a request aimed at browsing a resource; B. downloading (130) the requested resource; C. performing a syntactic parsing (135) of the downloaded resource; D. extracting (150) from the downloaded resource one or more lists, if any, of selectable shortcuts pointing to portions inside or outside the downloaded resource through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of lists of selectable shortcuts on the basis of an ontology (245) corresponding to the type of resource; E. on the basis of the ontology (245) corresponding to the type of resource, building (225) a list of one or more lists of selectable shortcuts extracted in step D ordered according to a list prioritisation; F. extracting (150) from the downloaded resource one or more content elements through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of content elements on the basis of the ontology (245) corresponding to the type of resource; G. on the basis of the ontology (245) corresponding to the type of resource, building (290) a list of content elements extracted in step F ordered according to a content element prioritisation; H. on the basis of the lists built in steps E and G and on the basis of the ontology (245) corresponding to the type of resource, building a final structure of lists of selectable shortcuts and of content elements; I. playing (125) a voice prompt based on the final structure and starting a voice interaction with the user for browsing the resource.

Computer-implemented method of browsing a resource through voice interaction comprising the following steps: A. acquiring (100) from a user a request aimed at browsing a resource; B. downloading (130) the requested resource; C. performing a syntactic parsing (135) of the downloaded resource; D. extracting (150) from the downloaded resource one or more lists, if any, of selectable shortcuts pointing to portions inside or outside the downloaded resource through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of lists of selectable shortcuts on the basis of an ontology (245) corresponding to the type of resource; E. on the basis of the ontology (245) corresponding to the type of resource, building (225) a list of one or more lists of selectable shortcuts extracted in step D ordered according to a list prioritisation; F. extracting (150) from the downloaded resource one or more content elements through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of content elements on the basis of the ontology (245) corresponding to the type of resource; G. on the basis of the ontology (245) corresponding to the type of resource, building (290) a list of content elements extracted in step F ordered according to a content element prioritisation; H. on the basis of the lists built in steps E and G and on the basis of the ontology (245) corresponding to the type of resource, building a final structure of lists of selectable shortcuts and of content elements; I. playing (125) a voice prompt based on the final structure and starting a voice interaction with the user for browsing the resource.

A pause estimation model learning apparatus includes: a morphological analysis unit configured to perform morphological analysis on training text data to provide M types of information, M being an integer that is equal to or larger than 2; a feature selection unit configured to combine N pieces of information, among the M pieces of information, to be an input feature when a predetermined certain condition is satisfied, and select predetermined one of the N pieces of information to be the input feature when the certain condition is not satisfied, N being an integer that is equal to or larger than 2 and equal to or smaller than M; and a learning unit configured to learn a pause estimation model by using the input feature selected by the feature selection unit and a pause correct label.

A pause estimation model learning apparatus includes: a morphological analysis unit configured to perform morphological analysis on training text data to provide M types of information, M being an integer that is equal to or larger than 2; a feature selection unit configured to combine N pieces of information, among the M pieces of information, to be an input feature when a predetermined certain condition is satisfied, and select predetermined one of the N pieces of information to be the input feature when the certain condition is not satisfied, N being an integer that is equal to or larger than 2 and equal to or smaller than M; and a learning unit configured to learn a pause estimation model by using the input feature selected by the feature selection unit and a pause correct label.

A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.

Computer-implemented method of browsing a resource through voice interaction comprising the following steps: A. acquiring (100) from a user a request aimed at browsing a resource; B. downloading (130) the requested resource; C. performing a syntactic parsing (135) of the downloaded resource; D. extracting (150) from the downloaded resource one or more lists, if any, of selectable shortcuts pointing to portions inside or outside the downloaded resource through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of lists of selectable shortcuts on the basis of an ontology (245) corresponding to the type of resource; E. on the basis of the ontology (245) corresponding to the type of resource, building (225) a list of one or more lists of selectable shortcuts extracted in step D ordered according to a list prioritisation; F. extracting (150) from the downloaded resource one or more content elements through a syntactic analysis and/or a semantic analysis and/or a morphological-visual analysis of extraction of content elements on the basis of the ontology (245) corresponding to the type of resource; G. on the basis of the ontology (245) corresponding to the type of resource, building (290) a list of content elements extracted in step F ordered according to a content element prioritisation; H. on the basis of the lists built in steps E and G and on the basis of the ontology (245) corresponding to the type of resource, building a final structure of lists of selectable shortcuts and of content elements; I. playing (125) a voice prompt based on the final structure and starting a voice interaction with the user for browsing the resource.