Starting with NLP: The Basics of Natural Language Processing

Before I delve deeper, let me share a short story that sparked my fascination with Natural Language Processing (NLP). Let me take you back to the early 2000s when I first started using computers running on Windows 98. Back then, I was learning BASIC, a programming language, and since I didn't have a personal computer at home, I had to visit a browsing center to run my programs and save them on floppy disks. The internet was still a far-fetched concept for me.

At the browsing center, there were private cabins where people would work or play games. Some even used yahoo.com to search for information, but as a newcomer to computers, this digital world was a mystery to me.

One day, curiosity got the better of me, and I asked a fellow user what they were doing on the computer. He happened to be a local computer teacher, and my question piqued his interest. He explained that he was searching for articles on Yahoo and even gave me a brief overview of how it worked.

However, what truly amazed me was the idea that computers could understand our language. Until then, I had only known how to write commands, which my teacher explained were translated by the computer into actions. With my inquisitive mind, I couldn't help but ask, "How does a computer understand our language?"

The teacher patiently explained that when we type something into the Yahoo search bar and press enter, it breaks down our sentences into individual words and then searches for relevant results.

This concept completely transformed my understanding of how computers worked and ignited a lifelong fascination. Little did I know that my innocent question that day would set me on a path to explore the fascinating world of NLP – a field of computer science dedicated to teaching machines to understand, interpret, and generate human language, just like the Yahoo search engine I was so curious about.

In recent times, we have taken giant leaps(through GenAI) in this field, but I always believe in keeping the basics right. Here, I will discuss NLP from the grassroot level.

Let me take you straight into the topic,

What is Natural Language Processing ?

NLP (Natural Language Processing) is a branch of artificial intelligence that deals with how computers understand and use human language. The goal is to teach machines to interpret, process, and generate language in a useful way.

Simply put, the goal of NLP is to empower computers with the ability to understand and generate human language, unlocking a new era of intelligent applications that enhance our lives.

Natural Language Processing (NLP) aims to bridge the gap between human communication and machine understanding. It's a multi-faceted field with two primary focuses:

• Natural Language Understanding (NLU): Enabling computers to decipher the meaning behind our words, sentences, and even emotions embedded in language. This involves analyzing grammar, context, and intent.
• Natural Language Generation (NLG): Empowering computers to produce human-like language, whether it's a simple response to a query or a creative piece of writing.

At its core, Natural Language Processing (NLP) deals with teaching computers to understand and generate human language. This is broken down into four key components:

• Syntax: This refers to the rules that govern the structure and order of words in sentences. Syntax helps computers identify the proper arrangement of words and understand the relationships between them.
• Semantics: This component deals with the actual meanings of words and phrases in a given context. Semantics enables machines to comprehend the intended meaning behind the language, rather than just recognizing the individual words.
• Pragmatics: Pragmatics involves understanding how language is used in different social contexts and how meanings can change based on the situation. It helps machines interpret language considering the context, intent, and implications behind the words.
• Discourse: This component focuses on how sentences and paragraphs are connected to form a coherent narrative or conversation. Discourse analysis helps computers understand the flow and relationships between different parts of a text or dialogue.

How do we perform Natural Language Processing ?

Let me take an analogy to explain that, the basic idea lies in teaching a kid how to talk. So, we keep talking to the kid often so that they can start associating sounds with objects, actions, and emotions.

Similarly, in Natural Language Processing (NLP), we expose computers to vast amounts of text data so that they can learn the patterns and relationships between words and their meanings.

But in essence, these are the steps which we follow to teach computers. I will explain these terms in details as we move on to the next section.

Data Collection is about gathering raw text data from diverse sources like documents, websites, or social media. Crucial for ensuring representation and avoiding bias in subsequent analysis.

• Web Scraping: Extracting raw text data directly from websites, often using tools or libraries designed to navigate and parse website content.
• APIs: Utilizing Application Programming Interfaces provided by various platforms (e.g., Twitter, Reddit) to access their text data in a structured format.
• Public Datasets: Leveraging pre-collected and readily available datasets specifically designed for NLP research or tasks.
• Database: Accessing structured text data stored within databases, either proprietary or publicly available.
• Documents: Utilizing individual text documents like books, articles, or reports as a source of data.

Preprocessing is a crucial initial step in NLP that transforms raw text data into a cleaner and more structured format suitable for analysis and model training. This process involves various techniques aimed at improving the quality and consistency of the data. By cleaning and standardizing the text, preprocessing enhances the accuracy, efficiency, and effectiveness of NLP tasks such as text classification, sentiment analysis, machine translation, and information retrieval.

• Text Cleaning: This step involves removing or correcting any errors, inconsistencies, or irrelevant information from the text data. This includes removing punctuation marks, special characters, HTML tags, extra white spaces, and stop words (common words like "the," "is," "if," etc., which don't add much meaning).
• Tokenizing: Text data is broken down into smaller units called tokens, typically words or subwords (parts of words). This step enables individual word analysis, which is essential for many NLP tasks.
• Normalizing: In this step, the text is standardized by converting all characters to lowercase, correcting spelling errors, and expanding contractions (e.g., "can't" to "cannot"). This ensures uniformity in the text data, which is crucial for accurate analysis.
• Stemming: Words are reduced to their base or root form (e.g., "running" becomes "run"). This process groups related words together and reduces the dimensionality of the data, making it easier for NLP models to process.
• Filtering: Irrelevant words or tokens are removed from the text data based on their frequency, part of speech, or domain knowledge. This step helps focus the analysis on the most relevant information.
• Annotation: Metadata or labels are added to the text data, such as part-of-speech tags or sentiment labels. These annotations provide additional information that can improve the performance of NLP models.
• Text Truncation: In some cases, the text is shortened by removing words or characters to limit the input size for NLP models. This step ensures that the models focus on the most relevant parts of the text.

Modelling involves selecting the appropriate machine learning algorithm or technique based on the problem at hand and the available data. In simple words, It basically means we want to develop a software program, which would understand and learn from the patterns, like the way we teach our kids and we keep checking if the learning is correct and accurate.

Here are few actions essential during modelling phase.

• Split Data: It is to split the available data into training and testing/validation sets. This is a crucial step to ensure that the model is trained on a portion of the data and evaluated on a separate, unseen portion to assess its performance accurately.
• Hyperparameter Tuning: Hyperparameters are settings or configurations that need to be specified before training a machine learning model. Hyperparameter Tuning involves finding the optimal combination of these settings to maximize the model's performance on the validation set.
• Regularization: Regularization techniques, such as L1 (Lasso) or L2 (Ridge) regularization, are applied to prevent overfitting, which occurs when the model performs well on the training data but fails to generalize to new, unseen data.
• Model Inference & Evaluation: At this stage, the trained model is used to make predictions or inferences on the test/validation data. The model's performance is then evaluated using appropriate metrics, such as accuracy, precision, recall, or F1-score, depending on the problem type (e.g., classification, regression).
• Model Monitoring: Once the model is deployed in production, it's essential to monitor its performance continuously. This involves tracking the model's accuracy, checking for data drift (changes in the input data distribution), and adjusting or retraining the model as needed to maintain its effectiveness over time.

Why do we need Natural Language Processing ?

Natural language processing is the key to building interfaces that will allow humans to interact with computers as naturally as they do with other humans. - Bill Gates

If I am answering this, I would say:

It is required to build Natural Language Understanding capabilities. This is essential for building more advanced artificial intelligence systems that can comprehend and reason about human language, enabling applications like conversational AI, question-answering systems, and language-based decision support systems.

Some of the broader goals might include:

• Human-computer interaction: NLP enables more natural and intuitive communication between humans and computers. By understanding and generating human language, NLP allows us to interact with machines using our natural spoken or written language, rather than being limited to rigid programming languages or commands.
• Information extraction and text mining: With the massive amount of unstructured text data available (e.g., news articles, social media posts, documents), NLP techniques are crucial for extracting valuable insights, knowledge, and information from this data. NLP enables tasks like named entity recognition, sentiment analysis, topic modeling, and summarization, making it easier to process and analyze large text corpora.
• Improved user experience: NLP powers many applications and services that enhance user experience, such as virtual assistants (e.g., Siri, Alexa), chatbots, machine translation, and text-to-speech systems. These NLP-enabled technologies make it easier for users to access information, complete tasks, and interact with digital services using natural language.
• Automated text processing: NLP is essential for automating tasks that involve processing and understanding large volumes of text data, such as document classification, spam filtering, content moderation, and plagiarism detection. This automation saves time and resources while ensuring accurate and consistent processing of text-based data.
• Accessibility and inclusivity: NLP can make digital content and services more accessible to individuals with disabilities or language barriers. For example, text-to-speech and speech-to-text technologies powered by NLP can assist users with visual or hearing impairments, while machine translation can bridge language gaps.

Conclusion

Natural Language Processing is a fascinating field that lies at the intersection of computer science, linguistics, and artificial intelligence. By teaching machines to understand and process human language, NLP has the potential to revolutionize the way we interact with technology and access information.

Nonetheless, the future of NLP is incredibly exciting. With the continuous advancements in machine learning, deep learning, and computational linguistics, we can expect to see even more impressive NLP-powered applications that will transform the way we live, work, and communicate.

Next steps .....

In the upcoming blog post, we will look into the practical aspects of Natural Language Processing (NLP) using Python. We'll explore how to gather data from YouTube. Once we have our dataset, we'll go through the essential preprocessing steps to clean and prepare the data for analysis.

Stay tuned for exciting updates and insightful content coming soon! In the meantime, I'd love to hear your thoughts and feedback. Your likes and comments help me understand what resonates with you, so I can create even better content in the future. Let's connect and build a community of passionate learners! ??

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