Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for resolving the intents of user expression. The exemplary embodiments may include receiving a user expression, receiving a first resolver having an input class and an output class based on the user expression, determining whether the first resolver can be resolved based on the user expression, and based on determining that the first resolver can be resolved based on the user expression, resolving the first resolver.

Abstract: A computer-implemented method, system and computer program product for automatically bootstrapping a domain-specific vocabulary from at least one source document using one or more computers, by: (a) encoding one or more passages in the source document to identify one or more relevant words therein, wherein the encoding assigns an importance to the relevant words using an attention mechanism (AM) on top of a recurrent neural network (RNN); (b) expanding the relevant words using word embedding distance, ontology information, or multi-part analogies; and (c) mapping the expanded words to concepts for inclusion into the domain-specific vocabulary, wherein concept disambiguation is performed to ensure that incorrect concepts are not included into the domain-specific vocabulary.

Abstract: A computer system generates a vector space model based on an ontology of concepts. One or more training examples are extracted for one or more concepts of a hierarchical ontology, wherein the one or more training examples for the one or more concepts are based on neighboring concepts in the hierarchical ontology. A plurality of vectors, each including one or more features, are initialized, wherein each vector corresponds to a concept of the one or more concepts. A vector space model is generated by iteratively modifying one or more vectors of the plurality of vectors to optimize a loss function. Natural language processing is performed using the vector space model. Embodiments of the present invention further include a method and program product for generating a vector space model in substantially the same manner described above.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for modifying multimedia. The exemplary embodiments may include receiving a multimedia and one or more inputs, determining a required amount of modification to the multimedia based on the one or more inputs, generating a literary parse tree based on the multimedia, extracting one or more node features from one or more nodes of the parse tree, determining a node importance of the one or more nodes based on applying a model to the one or more node features, and modifying one or more portions of the multimedia corresponding to the one or more nodes based on the node importance and the required amount of multimedia modification.

Abstract: A method for generating and presenting a comment excerpt in an online publication based on a comment in a comments section is provided. The method may include determining whether a passage from the comment in the comments section is relevant to a sentence in the online publication, and in response to determining that the passage from the comment is relevant to the sentence in the online publication, extracting the passage from the comment. The method may further include determining the scope of the comment that is associated with the extracted passage, wherein determining the scope of the comment comprises determining a context associated with the extracted passage based on text surrounding the extracted passage. The method may further include, based on the determined scope of the comment, generating the comment excerpt that corresponds to the comment. The method may further include presenting the comment excerpt within the online publication.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for resolving the intents of user expression. The exemplary embodiments may include receiving a user expression, receiving a first resolver having an input class and an output class based on the user expression, determining whether the first resolver can be resolved based on the user expression, and based on determining that the first resolver can be resolved based on the user expression, resolving the first resolver.

Abstract: A method for generating and presenting a comment excerpt in an online publication based on a comment in a comments section is provided. The method may include determining whether a passage from the comment in the comments section is relevant to a sentence in the online publication, and in response to determining that the passage from the comment is relevant to the sentence in the online publication, extracting the passage from the comment. The method may further include determining the scope of the comment that is associated with the extracted passage, wherein determining the scope of the comment comprises determining a context associated with the extracted passage based on text surrounding the extracted passage. The method may further include, based on the determined scope of the comment, generating the comment excerpt that corresponds to the comment. The method may further include presenting the comment excerpt within the online publication.

Abstract: A computer-implemented method, system and computer program product for automatically bootstrapping a domain-specific vocabulary from at least one source document using one or more computers, by: (a) encoding one or more passages in the source document to identify one or more relevant words therein, wherein the encoding assigns an importance to the relevant words using an attention mechanism (AM) on top of a recurrent neural network (RNN); (b) expanding the relevant words using word embedding distance, ontology information, or multi-part analogies; and (c) mapping the expanded words to concepts for inclusion into the domain-specific vocabulary, wherein concept disambiguation is performed to ensure that incorrect concepts are not included into the domain-specific vocabulary.

Abstract: A computer system generates a vector space model based on an ontology of concepts. One or more training examples are extracted for one or more concepts of a hierarchical ontology, wherein the one or more training examples for the one or more concepts are based on neighboring concepts in the hierarchical ontology. A plurality of vectors, each including one or more features, are initialized, wherein each vector corresponds to a concept of the one or more concepts. A vector space model is generated by iteratively modifying one or more vectors of the plurality of vectors to optimize a loss function. Natural language processing is performed using the vector space model. Embodiments of the present invention further include a method and program product for generating a vector space model in substantially the same manner described above.