Radar technical language modeling with named entity recognition and text classification

Jackson S. Zaunegger; Paul G. Singerman; Ram M. Narayanan; Sean M. O’Rourke; Muralidhar Rangaswamy

doi:10.1117/12.2622410

27 May 2022 Radar technical language modeling with named entity recognition and text classification

Jackson S. Zaunegger, Paul G. Singerman, Ram M. Narayanan, Sean M. O’Rourke, Muralidhar Rangaswamy

Proceedings Volume 12108, Radar Sensor Technology XXVI; 121080C (2022) https://doi.org/10.1117/12.2622410
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

This paper introduces the radar text data set (RadarTD) for technical language modeling. This data set is comprised of sentences containing radar parameters, values, and units determined from real-world values. This data set is created based on values determined from published academic research. Additionally, each statement is assigned a sentiment label and goal priority label. Preliminary investigations into the applicability of this data set are explored using the BERT model and several bi-directional LSTM models. These models are evaluated on text classification and named entity recognition tasks. This study evaluates the applicability of technical language modeling using neural networks to analyze input statements for cognitive radar applications. These findings suggest that this data set can be used to achieve reasonable performance for both text classification and named entity recognition for autonomous radar applications.

Conference Presentation

Citation Download Citation

Jackson S. Zaunegger, Paul G. Singerman, Ram M. Narayanan, Sean M. O’Rourke, and Muralidhar Rangaswamy "Radar technical language modeling with named entity recognition and text classification", Proc. SPIE 12108, Radar Sensor Technology XXVI, 121080C (27 May 2022); https://doi.org/10.1117/12.2622410

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