Abstract: The present invention is drawn to a raw material for chemical deposition for producing a ruthenium thin film or a ruthenium compound thin film by a chemical deposition method, containing an organoruthenium compound represented by the following formula 1, and further containing ?-diketone that is the same as a ligand of the organoruthenium compound. The raw material for chemical deposition of the present invention is inhibited in discoloration/precipitation even when heated at a high temperature, and enables to form a stable ruthenium thin film or ruthenium compound thin film. wherein substituents R1 and R2 are each hydrogen, or a linear or branched alkyl group.

Abstract: A device may obtain text to be analyzed to determine semantic connections between sections of the text. The device may identify subject-verb-object (SVO) units included in the text, and may determine SVO unit information that describes the SVO units. The device may analyze the SVO unit information to determine semantic connection information that identifies one or more semantic connections between two or more of the SVO units. The one or more semantic connections may identify relationships between verbs associated with the two or more of the SVO units. The device may generate a semantic network based on the SVO unit information and the semantic connection information, and may provide information regarding the semantic network.

Abstract: A device may receive text to be processed to prioritize text sections included in the text. The device may perform one or more prioritization techniques to prioritize the text sections. The one or more prioritization techniques may include at least one of: a semantic centrality analysis that indicates a degree to which a text section is semantically related to at least one other text section, an information content analysis that indicates a degree to which the text section includes information not included in the at least one other text section, or a combined analysis based on the semantic centrality analysis and the information content analysis. The device may prioritize the text sections, to form a set of prioritized text sections, based on performing the one or more prioritization techniques. The device may provide information that identifies the set of prioritized text sections.

Abstract: A device may receive information that identifies a search query to be used to search a text. The device may provide information that identifies multiple search query expansion techniques for expanding the search query. The device may receive a selection of one or more search query expansion techniques, of the multiple search query expansion techniques, to be performed to expand the search query. The device may perform the one or more search query expansion techniques to generate a set of expanded search queries based on the search query and the text. The device may search the text, using the set of expanded search queries, to identify multiple sections of the text that include an expanded search query included in the set of expanded search queries. The device may provide search results that identify the multiple sections of the text based on searching the text.

Abstract: A device may analyze text to identify a set of text portions of interest, and may analyze the text to identify a set of terms included in the set of text portions. The device may perform a similarity analysis to determine a similarity score. The similarity score may be determined between each term, included in the set of terms, and each text portion, included in the set of text portions, or the similarity score may be determined between each term and each other term included in the set of terms. The device may determine a set of dominant terms based on performing the similarity analysis. The set of dominant terms may include at least one term with a higher average degree of similarity than at least one other term. The device may provide information that identifies the set of dominant terms.

Abstract: A device analyzes first text to identify a pair of terms, within the first text, that are alias terms. The device analyzes the first text by performing two or more of: a latent semantic analysis of the pair of terms, based on the pair of terms being associated with a particular tag; a tag-based analysis that determines that the pair of terms are associated with compatible tags; a transitive analysis that determines that a pair of neighbor terms, associated with the pair of terms, are associated with compatible tags; or a co-location analysis based on a distance between the pair of terms in the first text. The device generates, based on analyzing the first text, a glossary that includes the pair of terms identified as alias terms. The device replaces terms within the first text or a second text that is different from the first text, using the glossary.

Abstract: A device may obtain text to be analyzed to determine semantic connections between sections of the text. The device may identify subject-verb-object (SVO) units included in the text, and may determine SVO unit information that describes the SVO units. The device may analyze the SVO unit information to determine semantic connection information that identifies one or more semantic connections between two or more of the SVO units. The one or more semantic connections may identify relationships between verbs associated with the two or more of the SVO units. The device may generate a semantic network based on the SVO unit information and the semantic connection information, and may provide information regarding the semantic network.

Abstract: A device may receive information that identifies a search query to be used to search a text. The device may provide information that identifies multiple search query expansion techniques for expanding the search query. The device may receive a selection of one or more search query expansion techniques, of the multiple search query expansion techniques, to be performed to expand the search query. The device may perform the one or more search query expansion techniques to generate a set of expanded search queries based on the search query and the text. The device may search the text, using the set of expanded search queries, to identify multiple sections of the text that include an expanded search query included in the set of expanded search queries. The device may provide search results that identify the multiple sections of the text based on searching the text.

Abstract: A device may receive text to be processed to prioritize text sections included in the text. The device may perform one or more prioritization techniques to prioritize the text sections. The one or more prioritization techniques may include at least one of: a semantic centrality analysis that indicates a degree to which a text section is semantically related to at least one other text section, an information content analysis that indicates a degree to which the text section includes information not included in the at least one other text section, or a combined analysis based on the semantic centrality analysis and the information content analysis. The device may prioritize the text sections, to form a set of prioritized text sections, based on performing the one or more prioritization techniques. The device may provide information that identifies the set of prioritized text sections.

Abstract: A device may analyze text to identify a set of text portions of interest, and may analyze the text to identify a set of terms included in the set of text portions. The device may perform a similarity analysis to determine a similarity score. The similarity score may be determined between each term, included in the set of terms, and each text portion, included in the set of text portions, or the similarity score may be determined between each term and each other term included in the set of terms. The device may determine a set of dominant terms based on performing the similarity analysis. The set of dominant terms may include at least one term with a higher average degree of similarity than at least one other term. The device may provide information that identifies the set of dominant terms.

Abstract: A device analyzes first text to identify a pair of terms, within the first text, that are alias terms. The device analyzes the first text by performing two or more of: a latent semantic analysis of the pair of terms, based on the pair of terms being associated with a particular tag; a tag-based analysis that determines that the pair of terms are associated with compatible tags; a transitive analysis that determines that a pair of neighbor terms, associated with the pair of terms, are associated with compatible tags; or a co-location analysis based on a distance between the pair of terms in the first text. The device generates, based on analyzing the first text, a glossary that includes the pair of terms identified as alias terms. The device replaces terms within the first text or a second text that is different from the first text, using the glossary.