Abstract: The present disclosure discloses a method for generating a styled sentence by a computer device. The method includes: obtaining a to-be-converted natural sentence, classifying, by inputting the natural sentence into a first encoding model, having a classification capability of classifying the natural sentence into a target content vector and a style vector of the natural sentence, the target content vector indicating a meaning of the natural sentence, and the style vector of the natural sentence indicating a language style of the natural sentence. The method also include determining, from at least one style vector according to a set target language style, a target style vector corresponding to the target language style; and inputting the target content vector and the target style vector into a first decoding model, and generating a styled sentence corresponding to the natural sentence.

Abstract: The automated social networking graph mining and visualization technique described herein mines social connections and allows creation of a social networking graph from general (not necessarily social-application specific) Web pages. The technique uses the distances between a person's/entity's name and related people's/entities names on one or more Web pages to determine connections between people/entities and the strengths of the connections. In one embodiment, the technique lays out these connections, and then clusters them, in a 2-D layout of a social networking graph that represents the Web connection strengths among the related people's or entities' names, by using a force-directed model.

Abstract: The present disclosure discloses a method for generating a styled sentence by a computer device. The method includes: obtaining a to-be-converted natural sentence, classifying, by inputting the natural sentence into a first encoding model, having a classification capability of classifying the natural sentence into a target content vector and a style vector of the natural sentence, the target content vector indicating a meaning of the natural sentence, and the style vector of the natural sentence indicating a language style of the natural sentence. The method also include determining, from at least one style vector according to a set target language style, a target style vector corresponding to the target language style; and inputting the target content vector and the target style vector into a first decoding model, and generating a styled sentence corresponding to the natural sentence.

Abstract: Embodiments of the present application disclose a mathematical processing method for a textual question, an apparatus, a computer device, and a computer storage medium. A numeral in a question text sequence is mapped to an expression word, a question text sequence including the expression word is encoded into a semantic vector, an expression is generated by using the semantic vector including question information, and the expression word in the expression is replaced with the mapped numeral, so that the obtained mathematical expression, compared with a preset fixed expression template, is more accurate, is more likely to meet question-answering requirements, and can improve intelligent question-answering accuracy.

Abstract: The present disclosure discloses a method for generating a styled sentence by a computer device. The method includes: obtaining a to-be-converted natural sentence, inputting the natural sentence into a first encoding model to filter style information in the natural sentence, and generating a target content vector corresponding to the natural sentence. The method also include determining, from at least one style vector according to a set target language style, a target style vector corresponding to the target language style; and inputting the target content vector and the target style vector into a first decoding model, and generating a styled sentence corresponding to the natural sentence.

Abstract: The present disclosure discloses a method for generating a styled sentence by a computer device. The method includes: obtaining a to-be-converted natural sentence, inputting the natural sentence into a first encoding model to filter style information in the natural sentence, and generating a target content vector corresponding to the natural sentence. The method also include determining, from at least one style vector according to a set target language style, a target style vector corresponding to the target language style; and inputting the target content vector and the target style vector into a first decoding model, and generating a styled sentence corresponding to the natural sentence.

Abstract: Embodiments of the present application disclose a mathematical processing method for a textual question, an apparatus, a computer device, and a computer storage medium. A numeral in a question text sequence is mapped to an expression word, a question text sequence including the expression word is encoded into a semantic vector, an expression is generated by using the semantic vector including question information, and the expression word in the expression is replaced with the mapped numeral, so that the obtained mathematical expression, compared with a preset fixed expression template, is more accurate, is more likely to meet question-answering requirements, and can improve intelligent question-answering accuracy.

Abstract: The automated social networking graph mining and visualization technique described herein mines social connections and allows creation of a social networking graph from general (not necessarily social-application specific) Web pages. The technique uses the distances between a person's/entity's name and related people's/entities names on one or more Web pages to determine connections between people/entities and the strengths of the connections. In one embodiment, the technique lays out these connections, and then clusters them, in a 2-D layout of a social networking graph that represents the Web connection strengths among the related people's or entities' names, by using a force-directed model.

Abstract: The automated social networking graph mining and visualization technique described herein mines social connections and allows creation of a social networking graph from general (not necessarily social-application specific) Web pages. The technique uses the distances between a person's/entity's name and related people's/entities names on one or more Web pages to determine connections between people/entities and the strengths of the connections. In one embodiment, the technique lays out these connections, and then clusters them, in a 2-D layout of a social networking graph that represents the Web connection strengths among the related people's or entities' names, by using a force-directed model.

Abstract: Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.

Abstract: Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.

Abstract: Techniques for displaying a relationship graph are described herein. In one example, a search term may be used to obtain a plurality of documents from a network, such as the Internet. A plurality of entities, and relationships between at least some of those entities, may be extracted from the documents. In an example user interface, representations of a plurality of entities may be displayed, such as by shapes (e.g., circles) labeled to identify people or organizations. Edges (e.g., lines) may be used to connect different representations of entities and to thereby indicate a relationship between the connected entities. In a particular example, input from movement of a cursor over an edge may result in display of a description of a relationship between the connected entities. In a further particular example, size of each entity may be related to a number of connections each has with others.

Abstract: Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.

Abstract: The automated social networking graph mining and visualization technique described herein mines social connections and allows creation of a social networking graph from general (not necessarily social-application specific) Web pages. The technique uses the distances between a person's/entity's name and related people's/entities names on one or more Web pages to determine connections between people/entities and the strengths of the connections. In one embodiment, the technique lays out these connections, and then clusters them, in a 2-D layout of a social networking graph that represents the Web connection strengths among the related people's or entities' names, by using a force-directed model.

Abstract: Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.