Potential of natural language processing for metadata extraction from environmental scientific publications
Authors: Blanchy Guillaume , Albrecht Lukas , Koestel John , Garré Sarah 
Published: July 2022

Abstract. Climate change will most likely lead to an increase of extreme weather events, including heavy rainfall with soil surface runoff and erosion. Adapting agricultural management practices that lead to increased infiltration capacities of soil has potential to mitigate these risks. However, effects of agricultural management practices (tillage, cover crops, amendment, …) on soil variables (hydraulic conductivity, aggregate stability, …) often depend on the pedo-climatic context. Hence, the only possibility to gather information needed to advise stakeholders on suitable management practices is to quantify such dependencies using meta-analyses of studies investigating this topic. As a first step, structured information from scientific publications needs to be extracted to build a meta-database, which then can be analyzed and recommendations can be given in dependence to the pedo-climatic context. Manually building such a database by going through all publications is very time-consuming. Given the increasing amount of literature, this task is likely to require more and more effort in the future. Natural language processing (NLP) facilitates this task, but it is not clear yet to which extent the extraction process is reliable or complete. In this work, two corpora of documents were used, which we refer to as the OTIM and the Meta corpus in the following. The OTIM corpus contains the source publications of the entries of the OTIM database of near-saturated hydraulic conductivity from tension-disk infiltrometer measurements (https://github.com/climasoma/otim-db). The Meta corpus is constituted of all primary studies from 36 selected meta-analyses on the impact of agricultural practices on sustainable water management in Europe. We focused on three NLP techniques: topic modeling, tailored regular expressions and dictionaries and the shortest dependency path. We used topic modeling to sort the individual source-publications of the Meta corpus into 6 topics (e.g. related to cover crops, biochar, …) with a coherence metric Cv ranging from 0.7 to 0.9; Then, we used tailored regular expressions and dictionaries to extract coordinates, soil texture, soil type, rainfall, disk diameter and tensions on the OTIM corpus. We found that the respective information could be retrieved with 56 % up to 100 % of all relevant information (recall) and with a precision between 83 % and 100 %. Finally, we extracted relationships between a set of practices keywords (e.g. ‘biochar’, ‘zero tillage’, …) and soil variables (e.g. ‘soil aggregate’, ‘hydraulic conductivity’, ‘crop yield’,…) from the source-publications’ abstracts of the Meta corpus using the shortest dependency path between them. These relationships were further classified according to positive, negative or absent correlations between the driver and soil property. This quickly provided an overview of the different driver-variable relationships and their abundance for an entire body of literature. Overall, we found that all three tested NLP techniques were able to support evidence synthesis tasks such as selecting relevant publications on a topic, extracting specific information to build databases for meta-analysis and providing an overview of relationships found in the corpus. While human supervision remains essential, NLP methods have the potential to support fully automated evidence synthesis that can be continuously updated as new publications become available.