Natural Language Processing
Executive Summary
Key Takeaways
- ✓ Natural language processing extracts structure and meaning from unstructured text, converting language into data a downstream finance process, machine learning or financial modelling, can use.
- ✓ NLP is distinct from generative AI, extraction rather than generation, and from machine learning, which operates on already-structured data rather than raw text.
- ✓ Common finance applications of NLP include extracting terms from a contract, structuring disclosures from a filing, and parsing commentary from an earnings call transcript.
- ✓ NLP output, like any extracted data, should be spot-checked against the original source document before downstream use, since extraction accuracy varies with document quality and structure.
Definition¶
Natural language processing (NLP) is the category of artificial intelligence technique that extracts structure and meaning from unstructured text, converting language into data a downstream process can use.
Distinguishing From Generative AI and Machine Learning¶
NLP performs functionally the opposite operation to generative AI: it extracts existing structure and meaning from text that already exists, rather than generating new text from a prompt. It is also distinct from machine learning, which operates on already-structured data rather than raw, unstructured text as its starting point.
Why It Matters to Finance¶
In finance, NLP commonly feeds structured data into a downstream machine learning model or financial model, extracting terms from a contract, structuring disclosures from a filing, or parsing commentary from an earnings call transcript, addressed in the broader technique landscape in Artificial Intelligence in Finance. Extracted output should be spot-checked against the original source, since accuracy varies with document quality and structure.
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Frequently Asked Questions
What is natural language processing?
The category of artificial intelligence technique that extracts structure and meaning from unstructured text, converting language, contract terms, transcripts, disclosures, into data a downstream process can use.
How does NLP differ from generative AI?
NLP extracts existing structure and meaning from text that already exists; generative AI produces new text or content from a prompt. The two perform functionally opposite operations on language.
What finance tasks commonly use NLP?
Extracting terms from a contract, structuring disclosures from a regulatory filing, and parsing commentary from an earnings call transcript are common applications, each converting unstructured text into data a downstream process can use.
Should NLP-extracted data be trusted without checking?
No. Extracted data should be spot-checked against the original source document before downstream use, since extraction accuracy varies with the source document's quality, formatting, and structural consistency.
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