Exploring the Value of Natural Language Processing for Urban Water Research
Ina Vertommen, Xin Tian, Tessa E. Pronk, Siddharth Seshan, Sotirios Paraskevopoulos, B.A. Wols
Abstract
Natural Language Processing (NLP), empowered by the most recent developments in Deep Learning, demonstrates its potential effectiveness for handling texts. Urban water research  benefits from both subfields of NLP, namely, Natural Language Understanding (NLU) and Natural Language Generation (NLG). In this work, we present three recent studies that use NLP for: (1) automated processing and responding to registered customer complaint within Dutch water utilities, (2) automated collection of up-to-date water-related information from the Internet, (3) extraction of key information about chemical compounds and pathogen characteristics from scientific publications. These applications, using the latest NLP models and tools (e.g., Rasa, Spacy), take into account studies on both water quality and quantity for the water sector. According to our findings, NLU and rule-based text mining are effective in extracting information from unstructured texts. In addition, NLU and NLG can be integrated to build a human-computer interface, such as a value-based Chabot to understand and address the demands made by customers of water utilities.