Optum Technology, Inc.

Optum, Inc.

Optum Services (ireland) Limited

Ayan Sengupta

Suman Roy

Amit Kumar

Michael Bridges

Gaurav Ranjan

Vijay Varma Malladi

Tanmoy Chakraborty

William Scott Paka

Ahmed Selim

Riccardo Mattivi

Shashi Kumar

Siddhartha Asthana

Saransh Chauksi

Zhijing J Liu

Souparna Das

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Natural language processing machine learning frameworks trained using multi-task training routines

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, generating predictions based at least in part on recognizing significant words in unstructured text. In an embodiment, a method is provided. The method comprises: generating a plurality of word-level tokens for an input unstructured textual data object; and for each word-level token: determining a significance type and a significance subtype for the word-level token by using a significance recognition machine learning model, and assigning a significance token label or an insignificance token label to the word-level token. The method further comprises: generating a label-based feature data object based at least in part on a subset of word-level tokens associated with the significance token label; generating a prediction data object for the input unstructured textual data object by providing the label-based feature data object to a first prediction machine learning model; and performing one or more automated prediction-based actions.

Significance-based prediction from unstructured text

Various embodiments provide for summarization of an interaction, conversation, encounter, and/or the like in at least an abstractive manner. In one example embodiment, a method is provided. The method includes generating, using an encoder-decoder machine learning model, a party-agnostic representation data object for each utterance data object. The method further includes generating an attention graph data object to represent semantic and party-wise relationships between a plurality of utterance data objects. The method further includes modifying, using the attention graph data object, the party-agnostic representation data object for each utterance data object to form a party-wise representation data object for each utterance data object. The method further includes selecting a subset of party-wise representation data objects for each of a plurality of parties. The method further includes decoding, using the encoder-decoder machine learning model, the subset of party-wise representation data objects for each party to form abstractive summary data object(s).

Supervised and unsupervised machine learning techniques for communication summarization

There is a need for more effective and efficient predictive natural language topic detection. This need can be addressed by, for example, solutions for performing sequential topic detection. In one example, a method includes determining a sequential topic distribution data object for the current document sequence, determining a current term-context correlation data object for the current document sequence, determining a current context-topic correlation data object for the current document sequence, determining an updated term-topic correlation data object based at least in part on the current context-topic correlation data object, determining topic modeling predictions based at least in part on the sequential topic distribution data object and the updated term-topic correlation data object, and performing prediction-based actions based at least in part on the topic modeling predictions.

Natural language processing techniques for sequential topic modeling

There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP-based document prioritization by utilizing joint sentiment-topic (JST) modeling. In one example, a method comprises identifying a JST latent distribution of the digital document that describes topic designation probabilities and sentiment designation probabilities for the digital document; determining, by processing the topic designation probabilities, a document-topic entropy measure for the digital document; determining, by processing the sentiment designation probabilities, a sentiment-topic entropy measure for the digital document; determining, by processing per-word inverse domain frequency measures for the digital, a document popularity measure for the digital document; generating the predicted document priority score based on the document-topic entropy measure, the sentiment-topic entropy measure, and the document popularity measure; and performing one or more prediction-based actions based on the predicted document priority score.

Natural language processing techniques using joint sentiment-topic modeling

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector machine learning models.

Graph-embedding-based paragraph vector machine learning models

There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP analysis by utilizing joint topic-sentiment (JST) modeling. In one example, a method comprises receiving a per-document topic distribution for a digital document, wherein the per-document topic distribution comprises a per-document topic correlation indication for each candidate topic designation; receiving a per-document topic-sentiment distribution for the digital document, wherein the per-document topic-sentiment distribution comprises a per-document topic-sentiment correlation indication for each topic-sentiment pair of a candidate topic designation and a candidate sentiment designation; generating, based at least in part on the per-document topic distribution and the per-document topic-sentiment distribution, a topic designation and a sentiment designation for each selected word in the digital document; and generating a JST modeling output based a on each topic designation and each sentiment designation.

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Noida, India

Ayan Sengupta