Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods

This paper illustrates how a combination of information extraction, machine learning, and NLP corpus annotation practice was applied to a problem of ranking vulnerability of structures (service boxes, manholes) in the Manhattan electrical grid. By adapting NLP corpus annotation methods to the task of knowledge transfer from domain experts, we compensated for the lack of operational definitions of components of the model, such as serious event. The machine learning depended on the ticket classes, but it was not the end goal. Rather, our rule-based document classification determines both the labels of examples and their feature representations. Changes in our classification of events led to improvements in our model, as reflected in the AUC scores for the full ranked list of over 51K structures. The improvements for the very top of the ranked list, which is of most importance for prioritizing work on the electrical grid, affected one in every four or five structures.