Information extraction from maintenance logs

A computer system for processing natural-language entries, the computer system comprising:

• a processor;
• and a storage device coupled to the processor and storing instructions, which when executed by the processor cause the processor to perform a method, the method comprising:
• obtaining a plurality of historical natural-language entries associated with a first domain;
• pre-processing the plurality of historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms;
• training a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain, wherein the training comprises learning weight values of one or more generic terms, wherein a weight value of a respective generic term indicates likelihood that the respective generic term is related to a trigger event;
• generalizing the machine learning model trained in the first domain, thereby allowing the machine learning model to be applied to a second domain;
• and processing a natural-language entry associated with the second domain using the generalized machine learning model to determine whether the natural-language entry associated with the second domain corresponds to the trigger event.

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for processing natural-language entries, the method comprising:

• obtaining a plurality of historical natural-language entries associated with a first domain;
• pre-processing the plurality of historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms;
• training a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain, wherein the training comprises learning weight values of one or more generic terms, wherein a weight value of a respective generic term indicates likelihood that the respective generic term is related to a trigger event;
• generalizing the machine learning model trained in the first domain, thereby allowing the machine learning model to be applied to a second domain;
• and processing a natural-language entry associated with the second domain using the generalized machine learning model to determine whether the natural-language entry associated with the second domain corresponds to the trigger event.