Science Question and Answer

Dataset Features:

• Context: Descriptive paragraphs, spanning diverse domains such as social media analytics, machine learning methodologies, fair division problems, and video alignment algorithms.
• Question: Questions extracted from the context that challenge a model’s ability to understand, infer, and retrieve key information.
• Answer: Short, precise answers to the corresponding questions, drawn directly from the context or requiring interpretative reasoning.
• QA_ID: A unique identifier for each entry, which can be used to track or reference specific rows.

Usage:

• Training and evaluating NLP models: Benchmarking algorithms for tasks such as information retrieval, question answering, and contextual inference.
• Feature analysis in text understanding: Identifying patterns in text comprehension and question-answer mapping.
• Data augmentation and pretraining: Enriching NLP datasets with diverse content and question-answer scenarios.

Coverage:

• Election and social media analysis
• Algorithmic advancements in AI and machine learning
• Mathematical frameworks for fairness and optimisation
• Video-to-language alignment
• Dimensionality reduction and robust PCA
• Heterogeneous information networks (HINs)
• Incomplete data querying and bag semantics
• This wide-ranging content makes it suitable for exploring domain-specific challenges and developing robust, generalisable models.

License:

Who Can Use It:

• NLP researchers and practitioners.
• Machine learning enthusiasts focusing on domain-specific text tasks.
• Students exploring applications of information retrieval and QA systems.

How to Use It:

• Develop and benchmark NLP models in QA tasks.
• Investigate the relationship between context complexity and answer predictability.
• Conduct a comparative analysis of algorithmic performance across domains.
• Train models to handle noisy, domain-specific, and multilingual data.