Librarians and faculty can search the Community of Online Research Assignments (CORA) for information literacy assignments, library instruction activities, and teaching tips.
The current state-of-the-art on Cora is ACNet. See a full comparison of 7 papers with code.
That’s the question many cora research paper classification college students ask themselves (and Google), and we can understand them. Even when a student is a great essay writer, they might still not have enough time to complete all the writing assignments on time or cora research paper classification do this well enough, especially when the exams are cora research paper classification near.The original paper describing CoRA dataset with 2708 nodes: Collective Classification in Network Data Original text files from 11881 papers belonging to 80 different categories Graph Convolutional Network on CoRA.Very good resources with detailed description. The personal page of Andrew McCallum Prof. McCallum in UMass has datasets such as Cora Information Extraction, Cora Research Paper Classification etc. Useful for co-citation and co-authorship network.
Dubbed as one of the milestones in deep learning, this research paper “ImageNet Classification with Deep Convolutional Neural Networks” started it all. Even though deep learning had been around since the 70s with AI heavyweights Geoff Hinton, Yann LeCun and Yoshua Bengio working on Convolutional Neural Networks, AlexNet brought deep learning into the mainstream.
Cora dataset. The Cora dataset is a citation graph where nodes represent machine learning papers and edges represent citations between pairs of papers. The task involved is document classification where the goal is to categorize each paper into one of 7 categories. In other words, this is a multi-class classification problem with 7 classes. Graph.
Cora Research Paper Classification (relational document classification) Research papers classified into a topic hierarchy with 73 leaves. We call this a relational data set, because the citations provide relations among papers.
CORA, the Cork Open Research Archive, gives you free open access to University College Cork's scholarly and scientific research publications and theses. UCC Research Communities Select a community to browse its collections.
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This data set is based on the cora data set (McCallum et al., 2000), which comprises computer science research papers. It includes the full citation graph as well as labels for the topic of each paper (and potentially sub- and sub-subtopics). There are seven possible labels.
A research paper is an expanded essay that presents your own interpretation or evaluation or argument. When you write an essay, you use everything that you personally know and have thought about a subject. When you write a research paper you build upon what you know about the subject and make a deliberate attempt to find out what experts know.
Abstract. This paper examines some possibilities of improving the CORA classification algorithm developed by M. Bongard in sixties. The algorithm is based on finding features of objects one needs to classify. The theoretical part of this study explains two main shortcomings of the CORA classification algorithm: (1) the algorithm rejects features that at least once appear in the opposite class.
Cora research paper classification refers to the task of classifying a set of research papers into their areas. From the original dataset, we extract four target sets, each of which includes papers from around four areas. The training sets contain research papers that are different from those in the target sets.
Our dataset is the paper citation network known as Cora where graph nodes represent research papers, and edges represent citation relationships between the papers. If a paper cites another paper then there is an edge between the two papers.
This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, text classification and information extraction that enables efficient spidering, populates topic hierarchies, and identifies informative text segments.