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Authors: Omid Jafari 1 ; Parth Nagarkar 1 ; Bhagwan Thatte 2 and Carl Ingram 3

Affiliations: 1 Computer Science Department, New Mexico State University, Las Cruces, NM, U.S.A. ; 2 Protos Software, Tempe, AZ, U.S.A. ; 3 Vigilant Technologies, Tempe, AZ, U.S.A.

Keyword(s): Natural Language Processing, Named Entity Recognition, Spacy.

Abstract: Named Entity Recognition (NER) is an important task that detects special type of entities in a given text. Existing NER techniques are optimized to find commonly used entities such as person or organization names. They are not specifically designed to find custom entities. In this paper, we present an end-to-end framework, called SatelliteNER, that its objective is to specifically find entities in the Satellite domain. The workflow of our proposed framework can be further generalized to different domains. The design of SatelliteNER includes effective modules for preprocessing, auto-labeling and collection of training data. We present a detailed analysis and show that the performance of SatelliteNER is superior to the state-of-the-art NER techniques for detecting entities in the Satellite domain.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Jafari, O.; Nagarkar, P.; Thatte, B. and Ingram, C. (2020). SatelliteNER: An Effective Named Entity Recognition Model for the Satellite Domain. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KMIS; ISBN 978-989-758-474-9; ISSN 2184-3228, SciTePress, pages 100-107. DOI: 10.5220/0010147401000107

@conference{kmis20,
author={Omid Jafari. and Parth Nagarkar. and Bhagwan Thatte. and Carl Ingram.},
title={SatelliteNER: An Effective Named Entity Recognition Model for the Satellite Domain},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KMIS},
year={2020},
pages={100-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010147401000107},
isbn={978-989-758-474-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - KMIS
TI - SatelliteNER: An Effective Named Entity Recognition Model for the Satellite Domain
SN - 978-989-758-474-9
IS - 2184-3228
AU - Jafari, O.
AU - Nagarkar, P.
AU - Thatte, B.
AU - Ingram, C.
PY - 2020
SP - 100
EP - 107
DO - 10.5220/0010147401000107
PB - SciTePress