Papers by Bimal Bhattarai
ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification (2022.lrec-1)
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| Challenge: | Recent advances in natural language processing (NLP) have reshaped the industry . complexity of such models makes them a “black box” and can cause ethical concerns . |
| Approach: | They propose a convolutional TM architecture that breaks down text into a sequence of fragments . they propose to use a tokenization scheme to bind the tokens to the text fragments. |
| Outcome: | The proposed architecture improves on a set of text fragments and eliminates the need for a corpus-specific vocabulary. |
Tsetlin Machine Embedding: Representing Words Using Logical Expressions (2024.findings-eacl)
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| Challenge: | Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing. |
| Approach: | They propose to embed words in vector space using propositional logic instead of dense vectors . they evaluate embeddings on intrinsic and extrinsic benchmarks and visualize word clusters based on their results . |
| Outcome: | The proposed model outperforms GLoVe on six classification tasks. |
Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment (2022.lrec-1)
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| Challenge: | Existing models for fake news classification are difficult to explain and quality-assure . however, they are black-box-based and lack a clear explanation of their decisions. |
| Approach: | They propose an interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM) they use conjunctive clauses to capture lexical and semantic properties of both true and fake news text and use clause ensembles to calculate the credibility of fake news. |
| Outcome: | The proposed framework outperforms baseline models on PolitiFact and GossipCop datasets in terms of accuracy and provides higher F1-score than BERT and XLNet, but lower accuracy. |