Abstract: Technologies related to generating diverse electronic summary documents for a webpage are described herein. A sequence of tokens is extracted from the webpage, and the sequence of tokens is provided to several computer-implemented models. The computer-implemented models output respective sets of candidate assets based upon the sequence of tokens, where the candidate assets are potentially included in an electronic summary document for the webpage. Subsequently, a user query is received, and at least one candidate asset from the candidate assets are selected for inclusion in the electronic summary document based upon the query. Thus, different electronic summary documents can be generated for the webpage when different queries are received.

Abstract: Technologies related to computer-implemented conditional language models (CLMs) are described. A first CLM is trained to generate output texts based upon input texts and conditions. Output texts generated by the first CLM are included in a training set, and a second CLM is trained based upon the training set. The second CLM is then configured to receive input text and a condition and generate an output text based upon the input text and the condition.

Abstract: Technologies related to generating diverse electronic summary documents for a webpage are described herein. A sequence of tokens is extracted from the webpage, and the sequence of tokens is provided to several computer-implemented models. The computer-implemented models output respective sets of candidate assets based upon the sequence of tokens, where the candidate assets are potentially included in an electronic summary document for the webpage. Subsequently, a user query is received, and at least one candidate asset from the candidate assets are selected for inclusion in the electronic summary document based upon the query. Thus, different electronic summary documents can be generated for the webpage when different queries are received.

Abstract: Mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. A plurality of entity records are generated within a knowledge graph for mined entity names from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity names. Pattern recognition is applied to an active document using an enterprise named entity recognition (ENER) system to identify potential entity names within the document that match a respective one of a plurality of entity records in the knowledge graph. One or more matching entity names are annotated within the document with information from the knowledge graph for the respective ones of the plurality of entity records. The annotated information is displayed with the active document.

Abstract: Mining of a set of enterprise source documents within an enterprise intranet is performed, by a plurality of knowledge mining toolkits, to determine a plurality of entity names. A plurality of entity records are generated within a knowledge graph for mined entity names from the entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity names. Pattern recognition is applied to an active document using an enterprise named entity recognition (ENER) system to identify potential entity names within the document that match a respective one of a plurality of entity records in the knowledge graph. One or more matching entity names are annotated within the document with information from the knowledge graph for the respective ones of the plurality of entity records. The annotated information is displayed with the active document.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by an enterprise named entity recognition (ENER) model, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Abstract: A server computing device, including memory storing a knowledge graph. The server computing device may further include a processor configured to receive a natural language input and generate a tokenized utterance based on the natural language input. Based on the tokenized utterance, the processor may select a predefined intention indicating a target ontology entity type of the natural language input. The processor may identify at least one input ontology entity token included in the tokenized utterance and may identify at least one relation between the predefined intention and the input ontology entity token. Based on the predefined intention, the at least one input ontology entity token, and the relation, the processor may generate a structured query. Based on the structured query and the knowledge graph, the processor may output an output ontology entity token having the target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities connected by a plurality of edges. The server computing device may further include a processor configured to generate a glossary file based on the knowledge graph. The glossary file may include a plurality of ontology entities included in the knowledge graph. The processor may receive a plurality of utterance templates. Each utterance template may include an utterance and a predefined intention. For each utterance template, the processor may generate one or more utterance template copies in which one or more ontology entities included in the utterance are replaced with one or more utterance template fields. The processor may generate a plurality of training utterances at least in part by filling the one or more utterance template fields of the one or more utterance template copies with respective ontology entities included in the glossary file.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities. The server computing device may further include a processor configured to receive a tokenized utterance including a plurality of words and one or more metadata tokens. The processor may extract a respective word embedding vector from each word included in the tokenized utterance. Based on a glossary file, the processor may determine a respective ontology entity type of each word included in the tokenized utterance. The processor may extract a character embedding vector from each character included in the tokenized utterance. Based on the plurality of word embedding vectors, the plurality of respective ontology entity types of the words, and the plurality of character embedding vectors, the processor may determine a predefined intention of the tokenized utterance using at least one recurrent neural network. The predefined intention may indicate a target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities connected by a plurality of edges. The server computing device may further include a processor configured to generate a glossary file based on the knowledge graph. The glossary file may include a plurality of ontology entities included in the knowledge graph. The processor may receive a plurality of utterance templates. Each utterance template may include an utterance and a predefined intention. For each utterance template, the processor may generate one or more utterance template copies in which one or more ontology entities included in the utterance are replaced with one or more utterance template fields. The processor may generate a plurality of training utterances at least in part by filling the one or more utterance template fields of the one or more utterance template copies with respective ontology entities included in the glossary file.

Abstract: A server computing device, including memory storing a knowledge graph. The server computing device may further include a processor configured to receive a natural language input and generate a tokenized utterance based on the natural language input. Based on the tokenized utterance, the processor may select a predefined intention indicating a target ontology entity type of the natural language input. The processor may identify at least one input ontology entity token included in the tokenized utterance and may identify at least one relation between the predefined intention and the input ontology entity token. Based on the predefined intention, the at least one input ontology entity token, and the relation, the processor may generate a structured query. Based on the structured query and the knowledge graph, the processor may output an output ontology entity token having the target ontology entity type.

Abstract: A server computing device, including memory storing a knowledge graph including a plurality of ontology entities. The server computing device may further include a processor configured to receive a tokenized utterance including a plurality of words and one or more metadata tokens. The processor may extract a respective word embedding vector from each word included in the tokenized utterance. Based on a glossary file, the processor may determine a respective ontology entity type of each word included in the tokenized utterance. The processor may extract a character embedding vector from each character included in the tokenized utterance. Based on the plurality of word embedding vectors, the plurality of respective ontology entity types of the words, and the plurality of character embedding vectors, the processor may determine a predefined intention of the tokenized utterance using at least one recurrent neural network. The predefined intention may indicate a target ontology entity type.