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Where is NLP used?
Before diving into the more technical aspects and processes of how Natural Language Processing is performed, an effective and straightforward way to get its main essence is by having in mind its real-world applications, with the most common being:
Text Summarization
Text summarization involves condensing lengthy documents or articles into shorter, concise versions while retaining the essential information. So, NLP can be used to automatically summarize long articles or documents, making it easier for people to grasp the main points without reading the entire text.
Use Cases: Text summarization is beneficial for users who want to quickly grasp the key points of a document without reading the entire content. It is used in news aggregation services, research, and content curation.
Example: An NLP-based tool can summarize a lengthy research paper, providing an executive summary that highlights the main findings and conclusions.
Language Translation
Language translation is a fundamental NLP application that involves converting text from one language into another, enabling people who speak different languages to communicate and access information. In simple terms, it consists of analyzing the input text, understanding its structure, and generating an equivalent translation in the desired language.
Use Cases: Language translation is widely used on platforms like Google Translate, allowing users to translate web content, documents, and conversations in real-time. It plays a critical role in breaking down language barriers in our interconnected world.
Example: You can use a language translation service to translate a webpage written in French into English, making it accessible to a broader audience.
Sentiment Analysis
Sentiment analysis, often referred to as opinion mining, is a valuable NLP application that involves determining the sentiment or emotional tone expressed in a piece of text, whether it's a customer review, a social media post, or any other form of written communication.
Use Cases: Sentiment analysis helps businesses and organizations understand how people feel about their products, services, or brand. By analyzing customer feedback, reviews, and social media comments, companies can gauge customer satisfaction, identify areas for improvement, and make data-driven decisions.
Example: If a customer posts a review saying, "The customer service was exceptional, and I'm thrilled with my purchase," sentiment analysis would recognize this as a positive sentiment.
Chatbots and Virtual Assistants
Chatbots and virtual assistants are AI-powered conversational agents designed to interact with users through natural language. Chatbots and virtual assistants like Siri and Alexa rely on NLP to understand and respond to user commands. They process spoken or written input, extract meaning from it, and provide appropriate responses. They can be text-based or voice-activated.
Use Cases: Chatbots and virtual assistants are used in customer support, e-commerce, and various applications. They can answer questions, provide recommendations, automate tasks, and offer a personalized user experience. Examples include Siri, Alexa, and customer service chatbots on websites.
Example: If you ask a virtual assistant like Siri, "What's the weather forecast for tomorrow?" it will respond with the weather information for your location.
How does NLP work?
So, taking a step back, we can recall that Natural Language Processing (NLP) is the technology behind computers' ability to understand and work with human language. To be achieved, this is done through a series of fundamental processes, each building upon the other. Next, we will have a brief look at each one of them.
The Basics of Text Processing
At the heart of NLP lies text processing, which is the initial step in making sense of human language. In essence, it is the process of breaking down a piece of text into smaller, more manageable parts to help computers analyze and work with text data effectively. It is achieved by performing the following fundamental actions:
Tokenization:
What is it? Tokenization is the process of breaking down a piece of text, such as a sentence or a document, into smaller units called tokens. Tokens are typically words or punctuation marks.
Why is it important? Tokenization is crucial because it allows a computer to treat words and punctuation as individual elements. This step makes it possible to analyze and process text more effectively.
Example: Consider the sentence "I love NLP." Tokenization would break this sentence into three tokens: "I," "love," and "NLP."
Stop Word Removal
What are stop words? Stop words are common words like "the," "and," "in," or "of" that appear frequently in text but often carry little meaningful information. In many NLP tasks, it's common to remove these words to focus on the more meaningful content.
Why is it important? Taking out common words makes the text data simpler and can help NLP tasks work better by showing which words are important.
Example: In the sentence "The quick brown fox jumps over the lazy dog," stop words like "the" and "over" would be removed.
Stemming and Lemmatization
What are they? Stemming and lemmatization are techniques used to reduce words to their base or root forms.
Why are they important? These techniques help ensure that different forms of a word are treated as the same word. This simplifies text analysis by grouping similar words.
Example: The words "running," "ran," and "runner" might all be reduced to the root form "run."
Lowercasing
What is it? Lowercasing involves converting all text to lowercase. For example, "Hello" becomes "hello."
Why is it important? Lowercasing ensures that words are treated the same regardless of their capitalization. It helps in standardizing the text data for analysis.
Handling Special Characters and Punctuation:
What is it? Special characters and punctuation, such as periods, commas, and exclamation marks, are often removed or handled in specific ways depending on the NLP task.
Why is it important? Special characters and punctuation may or may not carry meaningful information, depending on the context. They are often removed to simplify the text for analysis.
Example: Suppose we have a text sentence: "Wow!!! That was amazing!!!" In this example, we have 6 exclamation marks (!). Depending on the NLP task, we might choose to remove them or handle them differently:
- In a text summarization task, by removing them, the sentence becomes: "Wow That was amazing". This simplifies the text, making it easier for the computer to understand it and to complete the task.
- In a sentiment analysis task, we could handle them as an indicator of the intensity of a sentiment, meaning that the phrase “Wow!!! That was amazing!!!” is not only positive (for the sake of the example we consider this phrase as positive and not as a possible example of sarcasm), but it is also more positive than the alternative "Wow That was amazing".
Feature Extraction
Once the text is in a manageable format, the subsequent step in NLP involves extracting crucial features. In NLP, "features" are specific traits or elements drawn from text data, such as particular words, patterns, or structures. These features serve as cues or markers that machine learning models and algorithms rely on to better predict, generate, or comprehend human language. They act like pieces of a puzzle, aiding models and algorithms in piecing together language.
But still, the question remains; how do computers manage to analyze and comprehend this text, even after it's been cleaned? The answer lies in their inability to directly interpret text; computers fundamentally process information in numerical forms (primarily using 0s and 1s). This leads to the necessity of text encoding—an essential step in the NLP process that renders text data usable and processable for computers.
Text encoding
What is it? Encoding text involves representing the processed text in a numerical format, allowing computers to comprehend it. By encoding text, you convert words, phrases, documents, or structures into numerical representations, enabling NLP models to work with them. These numerical representations are then utilized as features for various NLP tasks.
While delving into the intricacies of text encoding's numerical representations exceeds the scope of this article aimed at simplifying NLP basics, we'll briefly explore three common techniques used to create these numerical representations or features: Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and N-grams.
Bag of Words (BoW)
In simplified terms, BoW is like creating a dictionary of words from a collection of texts. But instead of having the definition of each word next to it, you have a number that counts how many times this word appears in this collection of texts. This simple approach helps us turn messy text into something computers can understand better.
Imagine you have two sentences: "I love NLP" and "I adore machine learning." BoW would create a dictionary with unique words like "I," "love," "NLP," "adore," and "machine learning." Then, it counts how many times each word appears in each sentence, like this:
"I" : 2
"love" : 1
"NLP" : 1
"adore" : 1
"machine learning" : 1
This simple representation allows a computer to compare and analyze sentences based on word frequency. By creating a list of unique words and counting how many times each word appears in a text, BoW highlights the keywords that define the document's content. This simplifies text analysis and allows computers to recognize patterns, making it valuable for tasks like sentiment analysis, spam detection, and text classification, where you want to understand the keywords in a document. While it doesn't capture the word order or context, it provides a basic way for computers to work with and understand text data.
Term Frequency-Inverse Document Frequency (TF-IDF)
TF-IDF is another way to represent text data. It considers not only the frequency of a word in a document, like BoW, but also how unique that word is across all documents in a collection. In simpler terms, it gives more importance to words that are specific to a particular document.
For example, if "NLP" appears frequently in one document but rarely in others, TF-IDF will assign it a higher weight, to highlight the uniqueness and importance of this word within that document. This way, TF-IDF focuses on words that differentiate one document from another, in contrast to BoW, which counts how many times each word appears in a document without considering its uniqueness across all documents.
Imagine you have a large collection of documents, and you want to find out which words are most significant in each document. Some words may appear frequently in most documents (common words), like "the" or "and", and get high counts according to the BoW method. But these words don't tell you much about the document's content because they are common across many texts.
However, certain words might appear frequently in one specific document but rarely in others. These words are unique to that document and likely carry essential information about its subject. By assigning a higher weight to these unique words, TF-IDF helps you identify and emphasize the distinctive keywords that make each document special. This way, TF-IDF aids in information retrieval, text analysis, and document classification by focusing on words that differentiate one document from another.
N-grams
N-grams are a way to analyze the structure of sentences or texts. They break down text into chunks of words (like two-word pairs or three-word groups) and show which ones often go together. For instance, in the sentence "I love NLP," the 2-grams (or bigrams) are "I love" and "love NLP."
N-grams help capture the context and relationships between words in a text and help computers understand how words relate to each other in sentences. Imagine reading a story and knowing that words like "once upon a" often come together. N-grams do that for computers. This helps with things like predicting what word might come next in a sentence or understanding the overall meaning of a paragraph. It's like giving computers a hint about how words connect in our language, making text analysis more accurate and useful.
Language Models
And finally, let's delve into the cornerstone of NLP: Language models. They power computers' comprehension of language nuances contextually.
What are they? Language models within NLP are computational frameworks engineered to comprehend, create, and predict human language. Trained on extensive textual data, these models mimic human-like text generation by grasping patterns, word relationships, and contextual usage. They leverage statistical and probabilistic methods to unravel text structures and meanings, wielding a diverse skill set—from auto-completing sentences and translating languages to analyzing sentiments, summarizing content, and crafting coherent paragraphs.
In simpler terms: Think of them as language experts for computers.
What do they do? They learn how language works and its rules by studying a ton of text, like books, articles, or conversations.
Why? By doing this, they get good at figuring out what words usually come after others (patterns), how sentences are formed (grammar), and what makes sense in different contexts.
How? They utilize sophisticated mathematical frameworks and statistical patterns to learn the intricacies of language. They use complex algorithms that rely on probabilities and patterns observed in the vast amounts of text they've been trained on. For instance, they determine the likelihood of word sequences based on co-occurrences in training data (like N-Grams!). They also analyze word sequence frequencies, comprehending contextual word usage (as in BoW and TF-IDF!), thereby comprehending language syntax, semantics, and word relationships.
Language models can be as simple as predicting the next word in a sentence or as complex as generating entire paragraphs. For instance, given the sentence "Once upon a time, there was a ________________," a language model might suggest words like "princess," "dragon," or "castle" based on its training data. These models are vital for tasks such as answering questions, email sorting, language translation, text summarization, sentiment analysis, and even content creation, enhancing the efficiency of our digital world.
Language models have significantly evolved, incorporating deep learning techniques such as neural networks and other machine learning architectures to craft language representations. These representations can predict the next word in a sentence/paragraph or generate entirely new sentences/paragraphs, all learned from established patterns. These advancements occur through layers of intricate mathematical computations and exposure to diverse textual data, allowing models to refine their understanding of language nuances.
One of the most influential language models is GPT-3 (Generative Pre-trained Transformer 3), developed by OpenAI. Renowned for its ability to generate contextually relevant and remarkably coherent text, GPT-3 finds extensive application in chatbots, content generation, language translation, and more. Models like GPT-3 have dramatically elevated the capabilities of NLP systems, ushering in novel opportunities for AI-driven natural language understanding and generation.
Conclusion
In essence, the intricate processes of text processing, feature extraction, and language modeling within NLP unveil the unprecedented potential of computers to engage with human language. They serve as the bedrock for innovations that bridge the gap between human communication and machine understanding. NLP's prowess empowers applications like chatbots, translation tools, and beyond to not just comprehend but also adeptly generate text, mirroring the nuances of human cognition and communication.
Through these foundational concepts, the digital realm is revolutionized, offering seamless and intuitive interactions between humans and machines. NLP's evolution continually propels us toward a future where technology not only comprehends but truly converses, understands, and assists humans in unprecedented ways. It's not merely about deciphering text; it's about enabling technology to seamlessly integrate with human communication, revolutionizing our digital landscape and enhancing the way we interact, learn, and progress.
REFERENCES:
https://aws.amazon.com/es/what-is/nlp/
https://suryamaddula.medium.com/domains-of-artificial-intelligence-8046d0778f1a
https://www.freecodecamp.org/news/natural-language-processing-techniques-for-beginners/
https://heuristic-bhabha-ae33da.netlify.app/modelos-de-lenguaje-y-n-gramas.html
