NLPHub: an e-Infrastructure-based text mining hub

Coro G., Panichi G., Pagano P., Perrone E.

Text mining  Cloud computing  Computer Networks and Communications  e-Infrastructures  Text processing  Computational Theory and Mathematics  Computer Science Applications  Theoretical Computer Science  Natural language processing  Software-as-a-Service  Software  Open Science  web processing service 

Text mining involves a set of processes that analyze text to extract high-quality information. Among its large number of applications, there are experiments that tackle big data challenges using complex system architectures. However, text mining approaches are neither easy to discover and use nor easily combinable by end-users. Furthermore, they should be contextualized within new approaches to science (eg, Open Science) that ensure longevity and reuse of methods and results. This article presents NLPHub, a distributed system that orchestrates and combines several state-of-the-art text mining services that recognize spatiotemporal events, keywords, and a large set of named entities. NLPHub adopts an Open Science approach, which fosters the reproducibility, repeatability, and reusability of methods and results, by using an e-Infrastructure supporting data-intensive Science.NLPHubaddsOpenScience-compliance to the connected services through the use of representational standards for services and computations. It also manages heterogeneous service access policies and enables collaboration and sharing facilities. This article reports a performance assessment based on an annotated corpus of named entities, which demonstrates that NLPHub can improve the performance of the single-integrated processes by cleverly combining their output.

Source: Concurrency and computation (Online) (2020): e5986. doi:10.1002/cpe.5986

Publisher: John Wiley & Sons,, Chichester, UK , Regno Unito

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