Search Results for author:
Found 7 papers, 4 papers with code
Citation Recommendation for Research Papers via Knowledge Graphs
Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work.
Analysing the Requirements for an Open Research Knowledge Graph: Use Cases, Quality Requirements and Construction Strategies
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get a full overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work.
Cross-Domain Multi-Task Learning for Sequential Sentence Classification in Research Papers
Our approach outperforms the state of the art on full paper datasets significantly while being on par for datasets consisting of abstracts.
Coreference Resolution in Research Papers from Multiple Domains
We present the following contributions: (1) We annotate a corpus for coreference resolution that comprises 10 different scientific disciplines from Science, Technology, and Medicine (STM); (2) We propose transfer learning for automatic coreference resolution in research papers; (3) We analyse the impact of coreference resolution on knowledge graph (KG) population; (4) We release a research KG that is automatically populated from 55, 485 papers in 10 STM domains.
Ranked #1 on
Coreference Resolution
on STM-coref
(using extra training data)
Requirements Analysis for an Open Research Knowledge Graph
Current science communication has a number of drawbacks and bottlenecks which have been subject of discussion lately: Among others, the rising number of published articles makes it nearly impossible to get an overview of the state of the art in a certain field, or reproducibility is hampered by fixed-length, document-based publications which normally cannot cover all details of a research work.
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1. 0 (STEM-ECR v1. 0).
Domain-independent Extraction of Scientific Concepts from Research Articles
We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions.
Ranked #1 on
Scientific Concept Extraction
on STM-corpus
