A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.

A dataset comprising text-based messages can be accessed. Tokens for words and punctuation marks contained in the text-based messages can be generated. Each token corresponds to one word or one punctuation mark. A vector representation for each of a plurality of the tokens can be generated using natural language processing. A sequence of tokens corresponding to the text-based message can be generated for each of a plurality of the text-based messages in the dataset. Ones of the tokens that represent punctuation marks can be identified. An artificial neural network can be trained to predict use of the punctuation marks in sentence structures. The training uses the generated sequence of tokens and the vector representations for the tokens, in the sequence of tokens, that represent the punctuation marks.

The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

The present disclosure is directed to an agent automation framework that is capable of extracting meaning from user utterances and suitably responding using a search-based natural language understanding (NLU) framework. The NLU framework includes a meaning extraction subsystem capable of detecting multiple alternative meaning representations for a given natural language utterance. Furthermore, the NLU framework includes a meaning search subsystem that enables elastic confidence thresholds (e.g., elastic beam-width meaning searches), forced diversity, and cognitive construction grammar (CCG)-based predictive scoring functions to provide an efficient and effective meaning search. As such, the disclosed meaning extraction subsystem and meaning search subsystem improve the performance, the domain specificity, the inference quality, and/or the efficiency of the NLU framework.

Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.

Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.

Techniques are described herein for determining a long-form of an abbreviation using a machine learning based approach that takes into consideration both sequential context and structural context, where the long-form corresponds to a meaning of the abbreviation as used in a sequence of words that form a sentence. In some embodiments, word representations are generated for different words in the sequence of words, and a combined representation is generated for the abbreviation based on a word representation corresponding to the abbreviation, a sequential context representation, and a structural context representation. The sequential context representation can be generated based on word representations for words positioned near the abbreviation. The structural context representation can be generated based on word representations for words that are syntactically related to the abbreviation. The combined representation can be input to a classification neural network trained to output a label representing the long-form of the abbreviation.

Techniques are described herein for determining a long-form of an abbreviation using a machine learning based approach that takes into consideration both sequential context and structural context, where the long-form corresponds to a meaning of the abbreviation as used in a sequence of words that form a sentence. In some embodiments, word representations are generated for different words in the sequence of words, and a combined representation is generated for the abbreviation based on a word representation corresponding to the abbreviation, a sequential context representation, and a structural context representation. The sequential context representation can be generated based on word representations for words positioned near the abbreviation. The structural context representation can be generated based on word representations for words that are syntactically related to the abbreviation. The combined representation can be input to a classification neural network trained to output a label representing the long-form of the abbreviation.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.

A method of identifying text (e.g., a sentence or sentence portion) in a word processing text editor; automatically identifying a domain-specific deep-learning neural network that corresponds to an identified context, from among one or more domain-specific deep-learning neural networks; automatically identifying at least one suggested replacement word using the identified domain specific deep-learning neural network that corresponds to the identified context; and automatically controlling a display to display a user interface that includes functionality that presents prompt information that includes the at least one suggested replacement word. Changes for errors that are common in academic papers written by non-native speakers may be suggested.