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Pratheeksha Nair

3 accepted papers

2024

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Pratheeksha Nair, Javin Liu, Catalina Vajiac, Andreas Olligschlaeger, Duen Horng Chau, Mirela Cazzolato +3
AAAI 2024technical

Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions of people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically co…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2023

SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking

Javin Liu, Hao Yu, Vidya Sujaya, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany
EMNLP 2023long findings

In this work, we propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting person names from noisy escort advertisements. Our method combines the simplicity of rule-matching (through antirules, i.e., negated rules) and the generalizability…

Cited by 0Source ↗Scholar ↗
2022

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li, Pratheeksha Nair, Kellin Pelrine, Reihaneh Rabbany
ACL 2022findings

Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising multiple people in the same ad is a strong indicator of trafficking. Thus, extracting person names from the text of these ads can provide valuable clues for furthe…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗