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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Published on: February 23, 2019

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

Valentin Tablan¹, Ian Roberts, Hamish Cunningham

¹Department of Computer Science, University of Sheffield, , Regent Court, 211 Portobello, Sheffield S1 4DP, UK. v.tablan@sheffield.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|December 12, 2012

Summary

This summary is machine-generated.

GATECloud.net is a new cloud platform making large-scale natural language processing (NLP) research accessible. It simplifies complex NLP tasks on the Amazon cloud, enabling data-intensive scientific experiments.

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Area of Science:

Computer Science
Computational Linguistics
Scientific Computing

Background:

Cloud computing facilitates the 'democratization of science' by providing scalable resources for data-intensive research.
Natural Language Processing (NLP) algorithms are complex, posing challenges for parallelization and cloud deployment.

Purpose of the Study:

To present GATECloud.net, a novel cloud-based platform for large-scale NLP research.
To enable researchers to conduct data-intensive NLP experiments using cloud computing resources.

Main Methods:

Developed a unique cloud-based platform, GATECloud.net, leveraging Amazon cloud infrastructure.
Addressed infrastructural complexities including load balancing, data management, deployment, security, and fault tolerance.
Included a cost-benefit analysis and usage evaluation of the platform.

Main Results:

GATECloud.net provides transparent management of infrastructural issues for researchers.
The platform harnesses on-demand compute power for efficient execution of NLP experiments.
A cost-benefit analysis and usage evaluation were conducted.

Conclusions:

GATECloud.net simplifies the execution of large-scale, data-intensive NLP research.
The platform effectively democratizes access to advanced computational resources for NLP scientists.
The study demonstrates the feasibility and benefits of cloud-based platforms for complex scientific research.