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NLPHub: An e‐Infrastructure‐based text mining hub
Author(s) -
Coro Gianpaolo,
Panichi Giancarlo,
Pagano Pasquale,
Perrone Erico
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5986
Subject(s) - computer science , reuse , set (abstract data type) , service (business) , reusability , data science , quality (philosophy) , world wide web , data mining , information retrieval , ecology , philosophy , economy , software , epistemology , economics , biology , programming language
Summary 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. NLPHub adds Open Science‐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.