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What is Natural Language Processing (NLP)?

Let’s break down the term “Natural Language Processing.” It consists of two parts: “Natural Language” and “Processing.”

Natural Language refers to the language we naturally use in everyday conversation. This isn’t the perfectly polished language we might use in formal writing, but rather the informal, often messy way we communicate, including slang, short forms, and even sarcasm. Processing means taking that language and transforming or analyzing it to produce a meaningful result.

When we combine these, Natural Language Processing (NLP) refers to the ability of machines to understand and interpret human language in a way that focuses more on what is meant rather than the exact words used.

All humans use NLP naturally to understand each other, picking up on context, tone, and even hidden meanings. This is why we can understand jokes or sarcasm, even if they’re subtle. However, this process is not limited to humans—machines and software can also be equipped with NLP. This is why AI chatbots, like ChatGPT, can understand and respond to questions, even if the grammar is incorrect.

In this article, we’ll explore what NLP is, particularly in the context of machine learning. We’ll discuss how it works, provide some examples, and highlight its benefits.

What Exactly is Natural Language Processing (NLP)?

According to Oracle, NLP is “a branch of artificial intelligence (AI) that allows computers to understand, generate, and manipulate human language.” It sits at the intersection of computer science, AI, and linguistics. NLP isn’t just about text, like with ChatGPT; it also covers speech, as seen with voice assistants like Siri.

NLP has two main areas: Natural Language Understanding (NLU) and Natural Language Generation (NLG). NLU focuses on understanding what is being said or written, turning unstructured data (like a sentence) into structured data that a computer can use. After understanding the input, NLG is used to generate a response in human language.

For example, when you ask ChatGPT a question, NLU helps the software understand what you’re asking, and NLG helps it craft a response that seems like it was written by a human.

Benefits of Natural Language Processing (NLP)

NLP has significantly advanced AI and machine learning, offering several benefits:

Faster, Large-Scale Analysis: NLP enables machines to quickly and efficiently analyze vast amounts of data, reducing errors and biases that are common with older technologies.
Cost-Effective Automation: NLP can automate tasks that were previously done manually, such as data analysis, translation, spam detection, and more. This reduces the need for hiring multiple specialists, saving costs.
Quick and Accurate Data Extraction: NLP can sift through large datasets to quickly find and extract specific information when needed.
Enhanced Customer Experience: NLP improves customer service by quickly resolving basic queries and providing 24/7 support. It can also transfer complex questions to a human agent when necessary.

Real-Life Examples of Natural Language Processing (NLP)

Here are some ways NLP is used in everyday technology:

Gmail: Gmail uses NLP for spam filtering by detecting suspicious words, tones, and grammar. It also categorizes emails into primary, social, and promotions, and offers predictive text suggestions.
Search Engines: Search engines use NLP to better understand search queries, correct typos, and rank results to provide the most relevant information.
Grammarly: Grammarly uses NLP to correct grammar and spelling, detect tone and style, and suggest alternative wordings.
Language Translation Software: NLP allows tools like Google Translate to understand the intended meaning of text and provide grammatically correct translations.

How Does NLP Work?

NLP relies on several techniques and tools, often referred to as NLP tasks. Before these tools are used, NLP goes through a preprocessing phase:

NLP Preprocessing:

Tokenization: This involves breaking down a text into smaller units called tokens. For example, the sentence “Emma is wearing a blue dress” would be split into tokens like “Emma,” “is,” “wearing,” “a,” “blue,” and “dress.”
Stemming and Lemmatization: Both aim to reduce words to their base forms, but in different ways. Stemming removes common word endings, though it might not always create meaningful words. Lemmatization, however, reduces words to their correct dictionary form.
Stop Word Removal: This process removes common filler words like “the,” “is,” and “of” to focus on more important content.

These steps are part of text normalization, which makes the text consistent and easier for NLP software to process.

NLP Tasks:

Part-of-Speech Tagging: This task tags each word in a text with its part of speech, like noun, verb, or adjective, to better understand its meaning.
Word Sense Disambiguation: This technique identifies the correct meaning of a word with multiple meanings based on context.
Sentiment Analysis: This task analyzes the sentiment or emotion behind a text, classifying it as positive, negative, or neutral.
Machine Translation: This involves translating text from one language to another while maintaining the original meaning.
Text Generation: A popular feature in NLP, text generation is used in AI tools like ChatGPT to create content ranging from poetry to blog posts.

Named-Entity Recognition: This process categorizes words in a text into specific categories like names, places, and dates, helping to understand the context better.

Challenges and Limitations of NLP

While NLP is powerful, it does have limitations:

Dependence on Training Data: NLP’s accuracy depends on the quality of the data it was trained on. If the data is biased or incorrect, it can produce flawed results.
Semantic Analysis Limits: Although NLP is good at understanding meanings, it struggles with non-verbal communication, such as body language or tone of voice.
Complex Inputs: NLP may misinterpret or struggle with texts full of slang, sarcasm, or ambiguity.

Conclusion

Natural Language Processing is a significant development in AI and machine learning, allowing machines to understand and interact with human language. While NLP has opened up new possibilities for human-computer communication, it still has room to grow. As technology advances, we may see NLP becoming even more sophisticated, potentially allowing machines to understand human emotions and intentions on a deeper level.