Papers with cross-domain applications
Can Large Language Models Identify Authorship? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving, but their potential in authorship analysis remains under-explored. |
| Approach: | They propose to integrate explicit linguistic features into LLMs to provide explanations into their reasoning processes. |
| Outcome: | The proposed models demonstrate their ability to perform zero-shot, end-to-end authorship verification effectively and provide explainability through explicit linguistic features. |
Ontology Matching Using Convolutional Neural Networks (2020.lrec-1)
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| Challenge: | a growing number of ontologies require effective ways to align different ontology models . traditional methods to align ontological models are based on string metrics and structure analysis . but convolutional neural networks can be applied as-is to any domain, allowing for cross-domain applications . |
| Approach: | They propose a method to align ontologies automatically using machine learning techniques . they use convolutional neural networks to perform string matching between class labels . |
| Outcome: | The proposed method achieves state-of-the-art on ontologies from the Ontology Alignment Evaluation Initiative (OAEI) it maintains good performance when tested on a different domain, which could lead to cross-domain applications. |