Jiaul Paik


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Resilience of Named Entity Recognition Models under Adversarial Attack
Sudeshna Das | Jiaul Paik
Proceedings of the First Workshop on Dynamic Adversarial Data Collection

Named entity recognition (NER) is a popular language processing task with wide applications. Progress in NER has been noteworthy, as evidenced by the F1 scores obtained on standard datasets. In practice, however, the end-user uses an NER model on their dataset out-of-the-box, on text that may not be pristine. In this paper we present four model-agnostic adversarial attacks to gauge the resilience of NER models in such scenarios. Our experiments on four state-of-the-art NER methods with five English datasets suggest that the NER models are over-reliant on case information and do not utilise contextual information well. As such, they are highly susceptible to adversarial attacks based on these features.


Using Zero-Resource Spoken Term Discovery for Ranked Retrieval
Jerome White | Douglas Oard | Aren Jansen | Jiaul Paik | Rashmi Sankepally
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies