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Developing NLP AND Supervised Machine Learning Techniques to Classify Mars Tasks

Abstract Number:

1412 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Adam Kurth (1), Mona Matar (2), Hunter Rehm (3)

Institutions:

(1) Arizona State University - Biodesign Institute, N/A, (2) NASA Glenn Research Center, Cleveland, OH, (3) HX5 LLC, NASA Glenn Research Center, Cleveland, OH

Co-Author(s):

Mona Matar  
NASA Glenn Research Center
Hunter Rehm  
HX5 LLC, NASA Glenn Research Center

First Author:

Adam Kurth  
Arizona State University - Biodesign Institute

Presenting Author:

Adam Kurth  
Arizona State University - Biodesign Institute

Abstract Text:

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), applied Natural Language Processing (NLP) and machine learning to classify 1,058 Mars tasks into 18 Human System Task Categories (HSTCs) [1]. We developed an NLP model using Google's BERT [2] to capture semantic and syntactic nuances. Supervised training on a subset of tasks improved classification accuracy for 9 of 18 HSTCs, especially when incorporating HSTC descriptions. To address severe class imbalance, we introduced weighting and sampling techniques for data augmentation [3]. Additionally, we fine-tuned BERT to implement a pairwise relatedness scoring method, enabling task clustering and progressing toward unsupervised labeling. This presentation covers data preprocessing, key syntax extraction using BERT, and supervised classification, highlighting NLP's potential for analyzing Mars mission tasks in crew health and performance studies.

Keywords:

Natural Language Processing|Machine Learning|BERT (Bidirectional Encoder Representations from Transformers)
|NASA Human Research Program (HRP)|Mars Task Classification|Supervised Learning

Sponsors:

Section on Risk Analysis

Tracks:

Risk Prediction

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