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Classification of arid soils for engineering purposes A pedological approach
Published in P.G. Fookes, R.H.G. Parry, Engineering Characteristics of Arid Soils, 2020
I.J. Smalley, T.A. Dijkstra, C.D.F. Rogers
Moore et al (1983) have noted that classification and related areas such as pattern recognition seem to be innate compulsive functions of the human brain, leading perhaps to psychological satisfaction to the classifier. Questions such as ‘What kind of soil is it?’ and ‘What is the name of that tree?’ are almost involuntary, even if the questioner is unlikely to be any wiser when supplied with the answer. Isbell (1992) states that there are two components of soil classification, just as there are in the classification of other natural phenomena. One is the grouping of like soils into classes, and the other is the assignment of an unknown or new entity to an appropriate existing class. This latter operation is better called identification (or allocation) and is usually most conveniently carried out by means of a key (Moore et al 1983). The term ‘taxonomy’ has been used as a synonym for ‘classification’. Engineers are allocaters and use a key (UCS or BS 5930) that supplies a soil name or symbol, which can be communicated to designer or constructor. Perhaps allocation skills would improve if more thought was given to the other aspect of soil classification, since that is one that has been signally neglected.
Computer Systems in Prevention and Diagnosis of Occupational Neurotoxicity
Published in Lucio G. Costa, Luigi Manzo, Occupatinal Neurotoxicology, 2020
Sarah R. Lloyd, A. H. Hall, Barry H. Rumack
Using such systems allows identification of common synonyms. Users may thus be able to recognize that “aethylmethylketon” (in the German literature) pertains to methyl ethyl ketone (MEK; 2-butanone), while the abbreviation TCE refers to trichloroethylene and not to tetrachloroethylene (PCE).
Exploration and Exploitation in Organizational Cybersecurity
Published in Journal of Computer Information Systems, 2022
SAS Text Miner 9.4 was used to convert the textual data from the 10-K reports into numerical data for topic modeling and analysis. To do so, various natural language processing (NLP) techniques were applied to parse and tokenize the text from 10-K reports and create a document-term matrix (DTM). The textual data was then cleaned for punctuation, numbers, and stop words. Next, morphological analysis was performed to remove suffixes from tokens to generate stems. The n-gram technique was applied to detect and recognize multi-word terms. Synonyms were identified and treated as the same term in the term table. Abbreviations, terms, and phrases on the term table that conveyed the same meaning were merged for consistent representation of words. Our cleaned DTM thus contained the frequency (i.e., number of times) each term appeared within the 10-K reports.
On the use of text augmentation for stance and fake news detection
Published in Journal of Information and Telecommunication, 2023
Ilhem Salah, Khaled Jouini, Ouajdi Korbaa
The synonyms are typically taken from a lexical database (i.e. dictionary of synonyms). WordNet (Shoemaker, 2019), used in our work for synonym replacement, is considered as the most popular open-source lexical database for the English language (Li et al., 2021).