An Open Science System for Text Mining
Authors: Gianpaolo Coro, Giancarlo Panichi, Pasquale Pagano (CNR-ISTI, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Text mining (TM) techniques can extract high-quality information from big data through complex system architectures. However, these techniques are usually difficult to discover, install, and combine. Further, modern approaches to Science (e.g., Open Science) introduce new requirements to guarantee reproducibility, repeatability, and re-usability of methods and results as well as their longevity and sustainability. In this paper, we present a distributed system (NLPHub) that publishes and combines several state-of-the-art text mining services for named entities, events, and keywords recognition. NLPHub makes the integrated methods compliant with Open Science requirements and manages heterogeneous access policies to the methods. In the paper, we assess the benefits and the performance of NLPHub on the I-CAB corpus.

Introduction
Today, text mining operates within the challenges introduced by big data and new Science paradigms, which require managing large volumes, high production rates, heterogeneous complexity, and unreliable content, while ensuring data and methods' longevity through re-use in complex models and process chains. Among the new paradigms, Open Science (OS) focuses on the implementation of the three "R"s of the scientific method: Reproducibility, Repeatability, and Re-usability (Hey et al., 2009; EU Commission, 2016). The systems envisaged by OS are based on Web services networks that support big data processing and the open publication of results. Although text mining techniques exist that can tackle big data experiments (Gandomi and Haider, 2015; Amado et al., 2018), few examples that incorporate OS concepts can be found (Linthicum, 2017). For example, common text mining "cloud" services do not allow easy repeatability of the experiments by different users and are usually domain-specific, thus poorly re-usable (Bontcheva and Derczynski, 2016; Adedugbe et al., 2018). Available multi-domain systems do not use communication standards (Bontcheva and Derczynski, 2016; Wei et al., 2016), and the few OS-oriented initiatives that use text mining focus specifically on document preservation and cataloguing (OpenMinTeD, 2019; OpenAire, 2019). In this paper, we present a multi-domain text mining system (NLPHub) that is compliant with OS and combines multiple and heterogeneous processes. NLPHub is based on an e-Infrastructure (e-I), i.e., a network of hardware and software resources that allow remote users and services to collaborate while supporting data-intensive Science through cloud computing (Pollock and Williams, 2010; Andronico et al., 2011). Currently, NLPHub integrates 30 state-of-the-art text mining services and methods to recognize fragments of text (annotations) associated with named abstract or physical objects (named entities), spatiotemporal events, and keywords. These integrated processes cover five languages (English, Italian, German, French, and Spanish), requested by the European projects this software is involved in (i.e., Parthenos, 2019; SoBigData, 2019; Ariadne, 2019). These processes come from different providers with different access policies, and the e-I is used both to manage this heterogeneity and to possibly speed up the processing through cloud computing. NLPHub uses the Web Processing Service standard (WPS, Schut and Whiteside, 2007) to describe all integrated processes and the Prov-O XML ontological standard (Lebo et al., 2013) to track the complete set of input, output, and parameters used for the computations (provenance). Overall, these features enable OS compliance, and we show that the orchestration mechanism implemented by NLPHub adds effectiveness and efficiency to the connected methods. The name "NLPHub" refers to the forthcoming extensions of this platform to other text mining methods (e.g., sentiment analysis and opinion mining) and natural language processing tasks (e.g., text-to-speech and speech processing).

Conclusion
We have described NLPHub, a distributed system connecting and combining 30 text processing methods for 5 languages that adds Open Science-oriented features to these methods. The advantages of using NLPHub are several, starting from the fact that it provides one single access endpoint to several methods and spares installation and configuration time. Further, it proposes the AMERGE process as a valid option when the best performing algorithm for a certain entity extraction is not known a priori. Also, the AMERGE-Keywords annotations can be used when the entities to extract are not known. Indeed, these features would require more investigation, especially through multiple-language experiments, in order to define their full potential and limitations. Finally, NLPHub adds to the original methods features like WPS and Web interfaces, provenance management, results sharing, and access/usage policies control, which make the methods more compliant with Open Science requirements. The potential users of NLPHub are scholars who want to use NERs but also want to avoid software and hardware-related issues, or automatic agents that need to automatically extract and reuse knowledge from large quantities of texts. For example, NLPHub can be used in automatic ontology population and - since it also supports Events extraction - automatic narratives generation (Petasis et al., 2011; Metilli et al., 2019). Future extensions of NLPHub will involve other text mining methods (e.g. sentiment analysis, opinion mining, and morphological parsing), and additional NLP tasks like text-to-speech and speech processing as-a-service.