Search Results for author: Md Shajalal

Found 7 papers, 0 papers with code

What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers

no code implementations24 Jul 2024 Md Shajalal, Md Atabuzzaman, Alexander Boden, Gunnar Stevens, Delong Du

Furthermore, the empirical user evaluation of the generated explanations concludes which important information needs to be considered in generating explanations in the context of fake review identification.

Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification

no code implementations31 Oct 2023 Md Shajalal, Sebastian Denef, Md. Rezaul Karim, Alexander Boden, Gunnar Stevens

Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.

Decision Making Deep Learning

From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer

no code implementations12 Oct 2023 Md. Rezaul Karim, Lina Molinas Comet, Md Shajalal, Oya Deniz Beyan, Dietrich Rebholz-Schuhmann, Stefan Decker

Domain experts often rely on most recent knowledge for apprehending and disseminating specific biological processes that help them design strategies for developing prevention and therapeutic decision-making in various disease scenarios.

Decision Making Knowledge Graphs +3

Arabic Sentiment Analysis with Noisy Deep Explainable Model

no code implementations24 Sep 2023 Md. Atabuzzaman, Md Shajalal, Maksuda Bilkis Baby, Alexander Boden

This paper proposes an explainable sentiment classification framework for the Arabic language by introducing a noise layer on Bi-Directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome over-fitting problem.

Arabic Sentiment Analysis Sentiment Classification

Textual Entailment Recognition with Semantic Features from Empirical Text Representation

no code implementations18 Oct 2022 Md Shajalal, Md Atabuzzaman, Maksuda Bilkis Baby, Md Rezaul Karim, Alexander Boden

In this paper, we propose a novel approach to identifying the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation.

Natural Language Inference Natural Language Understanding +2

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