Gentle introduction to Natural Language Processing

NLP is the backbone of the current development in large language models, natural language processing allows “chatbots” to reply in a natural way, thus making chatbots conversational rather than robotic.

Before we understand the NLP basics using an analogy, we must know some key terminology.

1. Tokenization/Tokens
2. Stop Words
3. Stemming and Lemmatization
4. TF-IDF
5. Word Embeddings

Let’s get started!

Imagine you are a robot scientist and want to build a conversational robot.

To build this you need to “teach” a robot about linguistics and rules of grammar for that you give a huge number of literary works by Hemsworth, Keats, and Wordsworth but it understands only numbers so for that we would start with breaking down the literary work into individual units like words or sentences this breaking down of data is called Tokenization and smaller units are called the token.

These tokens alone can’t explain grammar, so we move to more preprocessing steps to better understand the language.

These steps include:

• Removing Stop Words: Eliminating common words (e.g., “the”, “is”) that don’t contribute much meaning.
• Stemming/Lemmatization: Reducing words to their root form (e.g., “running” becomes “run” or “better” becomes “good”).
• Punctuation Removal: Stripping out unnecessary symbols or punctuation marks.
• Lowercasing: Converting all text to lowercase to avoid treating words like “Apple” and “apple” as different.

We are approaching our goal. The next step involves extracting information from the text preceding the robot. However, there is a challenge as our robot needs help understanding the language to convert words into numerical representations.

There are many algorithms by which we can represent the text data into numerical values some of the algorithms are as follows:

• Bag of Words (BoW): A simple technique where each document is represented by a count of words. Imagine a bag where you place all the words from a text, without any regard for word order or grammar. Each word is simply counted.
• TF-IDF: This adjusts the frequency of words by how unique or common they are across documents.
• Word Embeddings: More advanced representation, where words are converted into dense vectors (e.g., Word2Vec, GloVe) that capture their meanings based on context.

Hurray! Our robot understands the complexity of the language so now we can use it for multiple applications such as:

• Text Classification: Categorizing text into predefined labels (e.g., spam or not spam).
• Named Entity Recognition (NER): Detecting entities like names of people, places, or organizations.
• Sentiment Analysis: Identifying the sentiment (positive, negative, neutral) of a given text. (I have made a project on this do check it out here)
• Part-of-Speech Tagging: Assigning grammatical tags (noun, verb, etc.) to each word.
• Dependency Parsing: Analyzing sentence structure to understand relationships between words.

Finally, our robot outputs the results, which may require some post-processing steps. For example:

• Predictions: For tasks like sentiment analysis, the robot might output whether the text is positive, negative, or neutral.
• Generated Text: For text generation, the output would be a coherent sentence or paragraph.

Conclusion

Natural Language Processing (NLP) serves as a foundational element in developing conversational AI and large language models, transforming how machines interact with human language. As NLP continues to evolve, its applications — ranging from chatbots and sentiment analysis to machine translation — are shaping industries and improving the way we communicate with technology. With these tools, we’re one step closer to seamless human-machine interaction.

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To learn more about the concepts briefly explained you can click on the title of the concept to dive deeper into the world of NLP