Natural language processing is often used to analyze sentiment in artistic or political texts or to derive public opinion from social media posts; however, there is also great power in the use of natural language processing to extract semantic meaning of words from texts. By normalizing a raw text file, terms can be reduced to their unmodified forms by lemmatizing as well as categorized and tagged terms in accordance with parts of speech. A normalized text can be analyzed for syntactic structure to infer semantic meaning in context.<br/><br/>By taking advantage of grammatical patterns of language, definitions of terms can be identified as if one were "reading" the text. An ontology allows for a highly comprehensive and intuitive database to house the information. Along with each term's definition it shows relationships to other terms by means of a hierarchy. The relationships between these terms can be measured in vector space, thus quantifying said relationship between said terms and allowing for determination of certain characteristics such as similarity.<br/><br/>This poster will describe progress toward extraction of terms for knowledge representation from the IUPAC Recommendations published over the last 50+ years.

Text mining the IUPAC recommendations: Opportunities for knowledge discovery

256th National Meeting (2018)

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