513 citations · 1.4k across the 15 of their papers we have counts for
7 papers · 1 filter
Combining Distant and Direct Supervision for Neural Relation Extraction
Iz Beltagy, Kyle Lo, Waleed Ammar
In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism…
Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann +3
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, re…
Extracting Scientific Figures with Distantly Supervised Neural Networks
Noah Siegel, Nicholas Lourie, Russell Power +1
Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development…
Citation Count Analysis for Papers with Preprints
Sergey Feldman, Kyle Lo, Waleed Ammar
We explore the degree to which papers prepublished on arXiv garner more citations, in an attempt to paint a sharper picture of fairness issues related to prepublishing. A paper's c…
Construction of the Literature Graph in Semantic Scholar
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula +20
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting…
A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Dongyeop Kang, Waleed Ammar, Bhavana Dalvi +4
Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead…
Content-Based Citation Recommendation
Chandra Bhagavatula, Sergey Feldman, Russell Power +1
We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as cand…