Competing Algorithm Detection from Research Papers

Ganguly, Soumyajit; Pudi, Vikram

doi:10.1145/2888451.2888473

Cited by 2 publications

(2 citation statements)

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“…Candidate Phrase Extraction: To limit the search space of phrases, we propose to use noun phrases present in the Title and Abstract of document d as candidate phrases. For citation contexts, named entities form a better set of candidates as shown by (Ganguly and Pudi, 2016). However different named entities can be linked to different papers cited in the same citation context.…”

Section: Approach 21 Concept Extractionmentioning

confidence: 99%

“…So it becomes essential to first identify which entity e corresponds to which cited paper cp and then use the proposed algorithm to classify e as aim/method/result for the corresponding paper cp. For the above purpose, we use entity-citation linking algorithm (Ganguly and Pudi, 2016). The matching function iterates over entities and citations to get their closeness score.…”

Section: Approach 21 Concept Extractionmentioning

confidence: 99%

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Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature

Agrawal¹,

Mittal²,

Pudi³

2019

Proceedings of the Workshop on Extracting Structured Knowledge From Scientific Publications

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As scientific communities grow and evolve, there is a high demand for improved methods for finding relevant papers, comparing papers on similar topics and studying trends in the research community. All these tasks involve the common problem of extracting structured information from scientific articles. In this paper, we propose a novel, scalable, semi-supervised method for extracting relevant structured information from the vast available raw scientific literature. We extract the fundamental concepts of aim, method and result from scientific articles and use them to construct a knowledge graph. Our algorithm makes use of domain-based word embedding and the bootstrap framework. Our experiments show the domain independence of our algorithm and that our system achieves precision and recall comparable to the state of the art. We also show the research trends of two distinct communities-computational linguistics and computer vision.

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Section: Approach 21 Concept Extractionmentioning

confidence: 99%

Section: Approach 21 Concept Extractionmentioning

confidence: 99%