Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data-Reference-Cited by-同舟云学术

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Decoding the Obfuscated: Advanced NER Techniques on Commercial Sex Advertisement Data

Published:2023-12-13 Issue: Volume:abs/1609.08144 Page:144-151
ISSN:
Container-title:2023 International Conference on Computational Science and Computational Intelligence (CSCI)
language:
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Author:

Perez Alejandro Rodriguez¹,Rivas Pablo¹,Turek Javier²,Sooksatra Korn¹,Quevedo Ernesto¹,Bichler Gisela³,Cerny Tomas⁴,Giddens Laurie⁵,Petter Stacie⁶

Affiliation:

1. School of Eng. & Computer Science, Baylor University,Dept. of Computer Science

2. Intelligent Systems Research Intel Labs,Human-Robot Collaboration Lab

3. School of Criminology & Criminal Justice, California State University,San Bernardino

4. College of Engineering, The University of Arizona,Dept. of Systems and Industrial Eng.

5. G. Brint Ryan College of Business, University of North Texas,Information Tech. & Decisions Sci. Dept.

6. School of Business, Wake Forest University

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10590067/10590000/10590248.pdf?arnumber=10590248

Reference42 articles.

1. Googles neural machine translation system: Bridging the gap between human and machine translation;Wu;CoRR,2016

2. Distantly-supervised named entity recognition with noise-robust learning and language model augmented self-training;Meng,2021

3. Information Extraction in Illicit Web Domains

4. Character-based models for adversarial phone extraction: Preventing human sex trafficking;Chambers

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