TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE

Learning what to read: Focused machine reading

EMNLP 2017 • Enrique Noriega-Atala • Marco A. Valenzuela-Escarcega • Clayton T. Morrison • Mihai Surdeanu

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today's scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead... (read more)

PDF Abstract EMNLP 2017 PDF EMNLP 2017 Abstract

Code

Add Remove Mark official

No code implementations yet. Submit your code now

Tasks

Add Remove

READING COMPREHENSION

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

Add Remove

METHOD	TYPE
🤖 No Methods Found	Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet

Edit Social Preview

Learning what to read: Focused machine reading Edit social preview

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods used in the Paper Edit Add Remove