SPBERTQA: A Two-Stage Question Answering System Based on Sentence Transformers for Medical Texts

Nguyen, Nhung T. H.; Ha, Phuong Phan-Dieu; Nguyen, Luan Thanh; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.48550/arxiv.2206.09600

Cited by 1 publication

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“…Dense embedding is used by RoBERTa, but Multiple Negative Ranking (MNR) loss sentence embedding has surpassed recorded accuracies since 2019. MNR manipulates the cosine similarity metric by comparing opposing vectors (Nguyen et al, 2022). Spam datasets could be tested using this technique once the opensource implementation version becomes available.…”

Section: Implications For Further Researchmentioning

confidence: 99%

Establishing an Optimal Online Phishing Detection Method: Evaluating Topological NLP Transformers on Text Message Data

Milner

Baron

2023

JDSIS

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This research establishes an optimal classification model for online SMS spam detection by utilizing topological sentence transformer methodologies. The study is a response to the increasing sophisticated and disruptive activities of malicious actors. We present a viable lightweight integration of pre-trained NLP repository models with sklearn functionality. The study design mirrors the spaCy pipeline component architecture in a downstream sklearn pipeline implementation and introduces a user-extensible spam SMS solution. We leverage large-text data models from HuggingFace (roberta-base) via spaCy and apply linguistic NLP transformer methods to short-sentence NLP datasets. We compare the F1-scores of models and iteratively retest models using a standard sklearn pipeline architecture. Applying spaCy transformer modelling achieves an optimal F1-score of 0.938, a result comparable to existing research output from contemporary BERT/SBERT/‘black box’ predictive models. This research introduces a lightweight, user-interpretable, standardized, predictive SMS-spam detection model, that utilizes semantically similar paraphrase/ sentence transformer methodologies and generates optimal F1-scores for an SMS dataset. Significant F1-scores are also generated for a Twitter evaluation set, indicating potential real-world suitability.

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Section: Implications For Further Researchmentioning

confidence: 99%