What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a subfield of artificial intelligence that gives computers the ability to parse, interpret, and generate human language. It bridges the structured logic of computing systems and the ambiguous, context-dependent nature of human communication.
Historically, computers needed structured data (like a well-formatted SQL table) to do useful analysis. If a company received thousands of customer support emails, a traditional database could tell the company when the emails arrived, but not what the customers were complaining about. NLP addresses this limitation. It lets organizations extract structured, analyzable information from unstructured text such as legal contracts, social media posts, corporate wikis, and customer service transcripts.
Core NLP Capabilities
NLP is not a single algorithm; it is a set of capabilities for different language tasks.
1. Sentiment Analysis
Sentiment analysis is widely used in enterprise analytics. An NLP pipeline reads large volumes of social media posts or product reviews and scores each text as positive, negative, or neutral based on its content. This lets marketing teams track brand sentiment over time and spot quickly when a product launch is drawing negative reactions.
2. Named Entity Recognition (NER)
NER extracts categorized entities from raw text. When an NLP system reads a news article, it identifies the names of people, organizations, locations, and dates. Legal and financial teams use it to pull key details, such as the parties and dates in a merger, out of long filings with less manual reading.
3. Machine Translation and Summarization
NLP pipelines can translate technical documents between languages. They also perform extractive and abstractive summarization, for example reading a long earnings report and producing a one-paragraph executive summary. Summaries should still be checked against the source, especially for figures.
The Evolution of NLP Architectures
The technology behind NLP has changed considerably over the last decade.
Legacy NLP (Rules and Bag-of-Words)
Early NLP relied on hand-written rules or simple “bag-of-words” models. If the word “terrible” appeared three times in a review, the system scored it as negative. These models handled context and sarcasm poorly (e.g., “This movie is terribly good”).
The Transformer Architecture
Most modern NLP is built on the Transformer architecture, introduced by Google researchers in 2017. Transformers rely on a mechanism called self-attention.
When a Transformer reads a sentence, it processes all the tokens in parallel rather than one at a time. Self-attention computes how much each token should weigh every other token when building its representation. This lets the model distinguish that “bank” means something different in “the bank of the river” than in “I deposited money in the bank.” This contextual modeling underpins today’s large language models.
Summary of Technical Value
Natural language processing makes unstructured text available for analysis. Because much of an organization’s knowledge is stored as text, NLP provides the means to parse, organize, and analyze that information at scale. It supports automated document analysis, ongoing brand monitoring, and the generative AI and large language model applications built on top of it.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.