NLPHub: An e‐Infrastructure‐based text mining hub

Abstract

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.

Using semantic story maps to describe a territory beyond its map

“…Necessarily, automatic services to extract information from written stories and documents will be required to generate events out of written and multimedia documents automatically. Fortunately, the technology in this field is sufficiently mature to reach this target [10,21,22]. Therefore, automatic systems will use SMBVT asa-service to transform the extracted events into story maps and enrich the overall back-end knowledge base, while improving the possibility of extracting new knowledge.…”

“…For example, a D4Science VRE focusing on Marine Science can connect marine biologists with AI experts to include biological knowledge into AI models [16,19]. Similarly, VREs can manage independent communities, such as computational linguists [22] and geothermal energy scientists [53], that could eventually share methodologies for cross-application (e.g., to extract classification tags from geothermal documents). D4Science manages multiple access modalities to different VRE users (e.g., managers, members, system administrators).…”