Extracting Semantics from Maintenance Records

by   Sharad Dixit, et al.

Rapid progress in natural language processing has led to its utilization in a variety of industrial and enterprise settings, including in its use for information extraction, specifically named entity recognition and relation extraction, from documents such as engineering manuals and field maintenance reports. While named entity recognition is a well-studied problem, existing state-of-the-art approaches require large labelled datasets which are hard to acquire for sensitive data such as maintenance records. Further, industrial domain experts tend to distrust results from black box machine learning models, especially when the extracted information is used in downstream predictive maintenance analytics. We overcome these challenges by developing three approaches built on the foundation of domain expert knowledge captured in dictionaries and ontologies. We develop a syntactic and semantic rules-based approach and an approach leveraging a pre-trained language model, fine-tuned for a question-answering task on top of our base dictionary lookup to extract entities of interest from maintenance records. We also develop a preliminary ontology to represent and capture the semantics of maintenance records. Our evaluations on a real-world aviation maintenance records dataset show promising results and help identify challenges specific to named entity recognition in the context of noisy industrial data.


Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks

Neural networks (NNs) have become the state of the art in many machine l...

Improving Biomedical Pretrained Language Models with Knowledge

Pretrained language models have shown success in many natural language p...

TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models

Transferring knowledge from one domain to another is of practical import...

Structured information extraction from complex scientific text with fine-tuned large language models

Intelligently extracting and linking complex scientific information from...

Common-Knowledge Concept Recognition for SEVA

We build a common-knowledge concept recognition system for a Systems Eng...

Building astroBERT, a language model for Astronomy Astrophysics

The existing search tools for exploring the NASA Astrophysics Data Syste...

Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records

This paper presents a complete workflow designed for extracting informat...

Please sign up or login with your details

Forgot password? Click here to reset