Natural Language Processing

Natural language processing (NLP) is the branch of artificial intelligence that works with human language, written or spoken. A NIST researcher describes it as "a formal area of study that takes communications by humans and transforms that information into something more suitable for computer use and analysis." In broad terms, it works "by restructuring the communication into a form that allows it to be compared to 'concepts' or ideas that the computer has previously learned" (Michael Sharp, NIST Taking Measure blog, "Teaching Computers to Read 'Industry Lingo'," 10/26/2022).

Everyday uses

The same article names translation tools as the most common applications, including "language translations, such as English to Spanish" and "voice-to-text translation," and notes that "Interactive chatbots and some search engines use forms of NLP." GAO describes transformers, the network design behind large language models, as applying "widely to natural language processing" (GAO-24-106946). See Deep Learning and Large Language Model.

Where general NLP falls short

NIST contrasts NLP with technical language processing (TLP), "the act of using computers for capturing, understanding and translating jargon for other users." The article explains why general tools struggle with specialist text:

• "NLP tools trained for 'normal' speech just don't work in technical settings."
• "NLP defaults to the most common way to use a word, which often is incorrect."
• "Most NLP tools need numbers of examples in the hundreds of thousands to millions to teach them," and in many specialized settings the examples needed "just don't exist."
• "Misspellings, inconsistent shorthand, formatting differences and slang are all common occurrences in industrial documents."

The article's example is the abbreviation "TSP," which could stand for "'teaspoon,' or 'Thrift Savings Plan,' or 'trisodium phosphate,'" depending on context. It concludes that often "the only successful way" to teach a computer specialized language "is with direct human oversight and input."

Security

NIST AI 100-2e2025 reports "many advances in developing adversarial attacks on natural language processing (NLP) models," so text inputs can be crafted to mislead a model.

Why it matters for land and mining claim records

Land and mining claim documents are written in a technical dialect: abbreviations, legal descriptions, statutory terms, and names that recur in many spellings. NIST's point that general tools default to the most common meaning of a word is the reason names, dates, and descriptions pulled from records by software need review against the documents themselves.