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## Number of pages 28 Number of figures 7 ## Number of tables 12 Number of texts 4 S2 Table S1. Eleven major challenges identified in raw keyword data and their corresponding six-step preprocessing approaches developed in this study. Challenges Description ## Examples Corresponding preprocessing approaches (with inspection applied to all) Same stem but in different forms system vs. systems (system); contamination vs. ## contaminants (contamin) Standard word stemming. All keywords were lowercased and keywords with more than four letters were stemmed before other steps. Python NLP package nltk 1 and the "SnowballStemmer" algorithm was used. For example, contamination and contaminants were both normalized to their root contamin. A few words with irregular plural forms were manually corrected, such as bacterium (bacteria), consortium (consortia), and medium (media). Prefix or isomer 3,3'-dichlorobiphenyl vs. dichlorobiphenyl; alpha alumina vs. alumina Excess ending word lead concentration vs. lead; copper ion vs. copper Excess component removal. ChemListem, 2 a deep neural networks-based Python NLP package for chemical named entity recognition (NER), was adopted to pre-select organic c

Publisher

American Chemical Society (ACS)