American Express Travel Related Services Company, Inc.

Ravneet Ghuman

Sandeep Bose

Vinod Yadav

Madhu Sudhan Reddy Gudur

Mario Fragoso

Madhu Sudan Reddy Gudur

Ravi Batra

Suraj Madnani

Curtis T Merryweather

Ravi Varma

Shashank Kapoor

Rahul Panwar

Nikhil Jain

Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Naïve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

Identifying data of interest using machine learning

Disclosed are various embodiments for using decision trees for machine-learning when data is missing from a data set. A first attribute for splitting a plurality of records is identified. Then, the plurality of records are split into a first subset of records and a second subset of records. The first subset of records can include each of the plurality of records where a value is present for the first attribute and the second subset of records comprising each of the plurality of records where the value is absent for the first attribute. Finally, a node can be added to a decision tree that reflects the split of the plurality of records into the first subset of records and the second subset of records.

Decision tree learning with missing data

Disclosed are various embodiments for generating relevant notifications of content generated by third party data sources. In some embodiments, a system comprises a computing device and machine readable instructions. The computing device includes a processor and a memory. The machine-readable instructions can be stored in the memory that, when executed by the processor, cause the computing device to receive content from a third party data source based at least in part on a keyword topic. An embedding for the content is generated. The system can classify a portion of the content as associated with the keyword topic. Organizations can be identified from the portions of the content. The system can generate a list from the organizations identified in the content and transmit a notification to a client device regarding the content.

Relevant notifications of third party content

Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Na&#xef;ve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

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Phoenix, AZ, United States of America

Ravneet Ghuman