Chapter 09 · Sustainability & AISustainability & AI

Natural language processing (NLP)

Meaning statusEstablishedSource recordDirect document linkedWhy these are different

Definition

The field of AI concerned with analysing, understanding and generating human language, from documents and speech to dialogue.

References

Stanford / PearsonJurafsky & Martin — Speech and Language Processing (3rd ed. draft)

This source provides part of the technical or institutional basis for the definition.

NeurIPS 2020 / arXivLewis et al. — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

This source supports the explanation of how the term is applied, measured or governed in practice.

Overview

What it means

NLP ranges from information extraction and sentiment analysis to machine translation and question answering, now dominated by large pre-trained language models. Jurafsky and Martin's textbook remains the standard reference for the field's methods.

How it is used

For sustainability, NLP powers analysis of corporate disclosures, regulations, scientific literature and grievance data: screening thousands of reports for climate commitments, mapping supply-chain risks from text, and comparing stated policies against frameworks.

Why it matters

Most sustainability information is written language, not numbers. NLP makes that language machine-readable at scale — with the caveat that automated reading can miss nuance or repeat errors, so human review remains part of defensible analysis.