CORA Benchmark Dataset
A CORA Benchmark Dataset is a Research Dataset that is commonly used for text classification tasks, particularly in the context of academic and scientific documents.
- Example(s):
- Counter-Example(s):
- See: CORA Service, Data Snapshot, DBLP Dataset, Text Classification, Academic Documents.
References
2023
- (ChatGPT, 2023) ⇒ "What is a "CORA Dataset"? Explain in 2-3 paragraphs" prompt. ChatGPT, September 25 Version, OpenAI, 22 Oct. 2023. https://chat.openai.com.
- QUOTE: The CORA dataset typically refers to the CORA dataset used in the field of natural language processing and machine learning. CORA is a widely recognized and publicly available dataset that is primarily used for text classification and information retrieval tasks. It was created for evaluating and benchmarking the performance of various text classification algorithms, such as those used for document categorization or topic classification.
The CORA dataset consists of academic research papers or documents, often related to computer science and machine learning. Each document is associated with one or more categories or topics, making it a multi-label classification dataset. Researchers commonly use it to develop and test algorithms that can automatically classify documents into appropriate categories, making it a valuable resource for tasks like topic modeling, content recommendation, or citation analysis.
The dataset typically includes metadata such as author names, publication titles, and abstracts, and it's often used to build machine learning models that can predict the relevant topics or categories for a given document. Researchers and practitioners use CORA to develop and assess the effectiveness of various text classification and information retrieval techniques, making it an important resource in the field of natural language processing and information retrieval.
- QUOTE: The CORA dataset typically refers to the CORA dataset used in the field of natural language processing and machine learning. CORA is a widely recognized and publicly available dataset that is primarily used for text classification and information retrieval tasks. It was created for evaluating and benchmarking the performance of various text classification algorithms, such as those used for document categorization or topic classification.
2018
- (Fey et al., 2018) ⇒ Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Muller (2018). "SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels". In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 869-877).