PandaSearch: A fine-grained academic search engine for research documents

@article{Huang2015PandaSearchAF,
  title={PandaSearch: A fine-grained academic search engine for research documents},
  author={Feiran Huang and Jia Li and Jiaheng Lu and T. Ling and Zhaoan Dong},
  journal={2015 IEEE 31st International Conference on Data Engineering},
  year={2015},
  pages={1408-1411}
}
In the world of academia, research documents enable the sharing and dissemination of scientific discoveries. During these “big data” times, academic search engines are widely used to find the relevant research documents. Considering the domain of computer science, a researcher often inputs a query with a specific goal to find an algorithm or a theorem. However, to this date, the return result of most search engines is just as a list of related papers. Users have to browse the results, download… Expand
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References

SHOWING 1-6 OF 6 REFERENCES
A Dataset Search Engine for the Research Document Corpus
TLDR
This work proposes a framework to effectively identify datasets within the scientific corpus, and builds a user friendly web-based search interface for users to conveniently explore the dataset-paper relationships, and find relevant datasets and their properties. Expand
Extracting Keyphrases from Research Papers Using Citation Networks
TLDR
This work proposes CiteTextRank for keyphrase extraction from research articles, a graph-based algorithm that incorporates evidence from both a document's content as well as the contexts in which the document is referenced within a citation network. Expand
Single Document Keyphrase Extraction Using Neighborhood Knowledge
TLDR
This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. Expand
Fast and unified local search for random walk based k-nearest-neighbor query in large graphs
TLDR
FLoS (Fast Local Search) is presented, a unified local search method for efficient and exact top-k proximity query in large graphs based on the no local optimum property of proximity measures. Expand
Single-pass active learning with conflict and ignorance
TLDR
The results based on real-world binary and multi-class classification streaming data show that the single-pass active learning approach yields evolving classifiers whose performance is similar to that of classifiers using all samples for adaptation; however, the annotation effort in terms of the number of class label requests is reduced by up to 90 %. Expand
LIBSVM: A library for support vector machines
TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail. Expand