Abstract: Techniques are disclosed relating to natural language processing. In some embodiments, a computer system receives unlabeled content. In some embodiments, the computer system embeds, using a machine learning model, the unlabeled content, where the embedding generates an unlabeled vector. In some embodiments, the computer system determines, from a plurality of labeled vectors stored in a vector index, a first set of labeled vectors that match the unlabeled vector, where the first set of labeled vectors are generated from a set of labeled content stored in a database. In some embodiments, the computer system assigns a new label to the unlabeled content, where the new label is selected from the first set of labeled vectors. In some embodiments, the computer system stores the newly labeled content in the database. The disclosed techniques may advantageously provide for automatically labeling content based on its semantic rather than its syntactic meaning.

Abstract: Techniques are disclosed relating to natural language processing. In some embodiments, a computer system receives unlabeled content. In some embodiments, the computer system embeds, using a machine learning model, the unlabeled content, where the embedding generates an unlabeled vector. In some embodiments, the computer system determines, from a plurality of labeled vectors stored in a vector index, a first set of labeled vectors that match the unlabeled vector, where the first set of labeled vectors are generated from a set of labeled content stored in a database. In some embodiments, the computer system assigns a new label to the unlabeled content, where the new label is selected from the first set of labeled vectors. In some embodiments, the computer system stores the newly labeled content in the database. The disclosed techniques may advantageously provide for automatically labeling content based on its semantic rather than its syntactic meaning.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: A system and method for detecting anomaly behavior in interactive networks are described. An attributed bipartite graph related problem is generated. A graph convolutional memory network is developed based on the generated problem. A loss function is further developed based on the developed graph convolutional memory network. The developed graph convolutional memory network is trained to learn interaction patterns between different components. Anomalies are detected based on the trained developed graph convolutional memory network.

Abstract: Techniques are disclosed relating to natural language processing. In some embodiments, a computer system receives unlabeled content. In some embodiments, the computer system embeds, using a machine learning model, the unlabeled content, where the embedding generates an unlabeled vector. In some embodiments, the computer system determines, from a plurality of labeled vectors stored in a vector index, a first set of labeled vectors that match the unlabeled vector, where the first set of labeled vectors are generated from a set of labeled content stored in a database. In some embodiments, the computer system assigns a new label to the unlabeled content, where the new label is selected from the first set of labeled vectors. In some embodiments, the computer system stores the newly labeled content in the database. The disclosed techniques may advantageously provide for automatically labeling content based on its semantic rather than its syntactic meaning.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.

Abstract: A system and method for detecting anomaly behavior in interactive networks are described. An attributed bipartite graph related problem is generated. A graph convolutional memory network is developed based on the generated problem. A loss function is further developed based on the developed graph convolutional memory network. The developed graph convolutional memory network is trained to learn interaction patterns between different components. Anomalies are detected based on the trained developed graph convolutional memory network.