A method and system for causing a command-based personalized composite template to be displayed in a communication stream are disclosed, the personalized composite template including representations of first and second users combined with a representation of a selected template.

Techniques are disclosed for predicting a table column using machine learning. For example, a system can include at least one processing device including a processor coupled to a memory, the processing device being configured to implement the following: determining a local word density for words in a table, the local word density measuring a count of other words in a first region surrounding the words; determining a local numeric density for the words, the local numeric density measuring a proportion of digits in a second region surrounding the words; determining semantic associations for the words by processing the words using an ML-based semantic association model trained based on surrounding words in nearby table columns and rows; and predicting a table column index for the words by processing the table using an ML-based table column model trained based on the local word density, local numeric density, and semantic association.

Techniques are disclosed for predicting a table column using machine learning. For example, a system can include at least one processing device including a processor coupled to a memory, the processing device being configured to implement the following: determining a local word density for words in a table, the local word density measuring a count of other words in a first region surrounding the words; determining a local numeric density for the words, the local numeric density measuring a proportion of digits in a second region surrounding the words; determining semantic associations for the words by processing the words using an ML-based semantic association model trained based on surrounding words in nearby table columns and rows; and predicting a table column index for the words by processing the table using an ML-based table column model trained based on the local word density, local numeric density, and semantic association.

According to one implementation, a system includes a computing platform having processing hardware, and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a vocabulary, identify words from the vocabulary for use in extending the vocabulary, pair each of those words with every other of those words to provide word pairs, and output the word pairs to a vocabulary administrator. The software code also receives word pair characterizations identifying each of the word pairs as one of similar, dissimilar, or neither similar nor dissimilar, configures, based on the word pair characterizations, a multi-dimensional vector space including multiple embedding vectors each corresponding respectively to one of the identified words, and cross-references each of those words with its corresponding embedding vector to produce an extended vocabulary corresponding to the received vocabulary.

According to one implementation, a system includes a computing platform having processing hardware, and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a vocabulary, identify words from the vocabulary for use in extending the vocabulary, pair each of those words with every other of those words to provide word pairs, and output the word pairs to a vocabulary administrator. The software code also receives word pair characterizations identifying each of the word pairs as one of similar, dissimilar, or neither similar nor dissimilar, configures, based on the word pair characterizations, a multi-dimensional vector space including multiple embedding vectors each corresponding respectively to one of the identified words, and cross-references each of those words with its corresponding embedding vector to produce an extended vocabulary corresponding to the received vocabulary.

A method for performing a query based on a natural language voice input is provided. The method includes receiving, via a microphone, a voice input of a user, and converting the voice input into a first text data object. The method further includes converting the first text data object into a first technical language object using AI, and submitting a query based on the first technical language object. A query result in a second technical language object is retrieved in response to the query, and the query result is converted into a second text data object using AI. The method further converts the second text object into a voice data object indicating the query result, and outputs a voice signal to provide the information of the query result in a natural language form to the user.

A method for performing a query based on a natural language voice input is provided. The method includes receiving, via a microphone, a voice input of a user, and converting the voice input into a first text data object. The method further includes converting the first text data object into a first technical language object using AI, and submitting a query based on the first technical language object. A query result in a second technical language object is retrieved in response to the query, and the query result is converted into a second text data object using AI. The method further converts the second text object into a voice data object indicating the query result, and outputs a voice signal to provide the information of the query result in a natural language form to the user.

The efficiency of summary learning that requires an additional input parameter is improved by causing a computer to execute: a first learning step of learning a first model for calculating an importance value of each component in source text, with use of a first training data group and a second training data group, the first training data group including source text, a query related to a summary of the source text, and summary data related to the query in the source text, and the second training group including source text and summary data generated based on the source text; and a second learning step of learning a second model for generating summary data from source text of training data, with use of each piece of training data in the second training data group and a plurality of components extracted for each piece of training data in the second training data group based on importance values calculated by the first model for components of the source text of the piece of training data.

The efficiency of summary learning that requires an additional input parameter is improved by causing a computer to execute: a first learning step of learning a first model for calculating an importance value of each component in source text, with use of a first training data group and a second training data group, the first training data group including source text, a query related to a summary of the source text, and summary data related to the query in the source text, and the second training group including source text and summary data generated based on the source text; and a second learning step of learning a second model for generating summary data from source text of training data, with use of each piece of training data in the second training data group and a plurality of components extracted for each piece of training data in the second training data group based on importance values calculated by the first model for components of the source text of the piece of training data.

In a training data generation method, a computer executes: a generation step for generating partial data of a summary sentence created for text data; an extraction step for extracting, from the text data, a sentence set that is a portion of the text data, based on a similarity with the partial data; and a determination step for determining whether or not the partial data is to be used as training data for a neural network that generates a summary sentence, based on the similarity between the partial data and the sentence set. Thus, it is possible to streamline the collection of training data for a neural summarization model.