Image by author
We are learning a lot about ChatGPT and large language models (LLM). Natural language processing has been an interesting topic, a topic that is currently sweeping the world of technology and artificial intelligence. Yes, LLMs like ChatGPT have helped its growth, but wouldn’t it be nice to understand where it all comes from? So let’s get back to basics: NLP.
NLP is a subfield of artificial intelligence and is the ability of a computer to detect and understand human language, through speech and text, just as humans do. NLP helps models process, understand and generate human language.
The goal of NLP is to bridge the communication gap between humans and computers. NLP models are typically trained on tasks such as next word prediction, allowing them to build contextual dependencies and then generate relevant results.
The fundamentals of NLP revolve around being able to understand the different elements, characteristics and structure of human language. Think about the times you tried to learn a new language and had to understand different elements of it. Or if you haven’t tried learning a new language, maybe go to the gym and learn to do squats; You must learn the necessary elements to have good form.
Natural language is the way we, as humans, communicate with each other. Today there are more than 7,100 languages in the world. Wow!
There are some key fundamentals of natural language:
- Syntax – Refers to the rules and structures of word arrangement to create a sentence.
- Semantics – Refers to the meaning behind words, phrases and sentences in language.
- Morphology – Refers to the study of the actual structure of words and how they are formed from smaller units called morphemes.
- Phonology – It refers to the study of sounds in language and how the different units are formed together to combine words.
- Pragmatics – This is the study of how context plays an important role in the interpretation of language, for example tone.
- Speech – This is the connection between the context of language and how ideas form sentences and conversations.
- language acquisition – This is how humans learn and develop language skills, for example, grammar and vocabulary.
- Language variation – This focuses on the more than 7,100 languages spoken in different regions, social groups and contexts.
- Ambiguity – Refers to words or sentences with multiple interpretations.
- Polysemy – Refers to words with multiple related meanings.
As you can see, there are a variety of key fundamental elements of natural language, and all of them are used to direct language processing.
Now we know the fundamentals of natural language. How is it used in NLP? There is a wide range of techniques used to help computers understand, interpret and generate human language. These are:
- Tokenization – This refers to the process of breaking down or breaking down paragraphs and sentences into smaller units so that they can be easily defined for use in NLP models. Plain text is divided into smaller units called Tokens.
- Part of speech tagging – This is a technique that involves assigning grammatical categories, for example, nouns, verbs and adjectives to each element of a sentence.
- Named Entity Recognition (NER) – This is another technique that identifies and classifies named entities, for example, names of people, organizations, places and dates in text.
- Analysis of feelings – It is a technique that analyzes the tone expressed in a text, for example, whether it is positive, negative or neutral.
- Text classification – This is a technique that categorizes text found in different types of documentation into predefined classes or categories based on their content.
- ??Semantic Analysis – This is a technique that analyzes words and sentences to better understand what is being said using context and relationships between words.
- Word embeddings – This is when words are represented as vectors to help computers understand and capture the semantic relationship between words.
- Text generation – is when a computer can create human-like text based on learning patterns from existing text data.
- translation machine – This is the process of translating a text from one language to another.
- Language modeling – This is a technique that takes into account all the previous tools and techniques. It involves building probabilistic models that can predict the next word in a sequence.
If you’ve worked with data before, you know that once you collect it, you’ll need to standardize it. Data standardization involves converting data into a format that computers can easily understand and use.
The same applies to NLP. Text normalization is the process of cleaning and standardizing text data into a consistent formation. You’ll want a format that doesn’t have a lot of variation or noise. This makes it easier for NLP models to analyze and process language more effectively and accurately.
Before you can add anything to your NLP model, you need to understand computers and understand that they only understand numbers. Therefore, when you have text data, you will need to use text vectorization to transform the text into a format that the machine learning model can understand.
Take a look at the image below:
Image by author
Once the text data is vectorized into a format that the machine can understand, the NLP machine learning algorithm receives training data. This training data helps the NLP model understand the data, learn patterns, and establish relationships over the input data.
Statistical analysis and other methods are also used to build the model’s knowledge base, which contains text features, different features, and more. It is basically a part of your brain that has learned and stored new information.
The more data that is fed into these NLP models during the training phase, the more accurate the model will be. Once the model has gone through the training phase, it will be put to the test during the testing phase. During the testing phase, you will see how accurately the model can predict outcomes using unseen data. Unseen data is new data to the model, so you must use its knowledge base to make predictions.
Since this is a basic overview of NLP, I have to do exactly that and not lose you with overly heavy terminology and complex topics. If you want to know more, read:
You now have a better understanding of the fundamentals of natural language, the key elements of NLP, and how it loosely works. Below is a list of applications of NLP in today’s society.
- Analysis of feelings
- Text classification
- Language translation
- Chatbots and virtual assistants
- Speech recognition
- Recover of information
- Named Entity Recognition (NER)
- Topic modeling
- Text Summary
- Language generation
- Spam detection
- Answer to questions
- Language modeling
- Fake news detection
- Medical and healthcare NLP
- Financial analysis
- Analysis of legal documents
- Emotion analysis
There have been many recent developments in NLP, as you may already know with chatbots like ChatGPT and large language models appearing left, right and center. Learning about NLP will be very beneficial for anyone, especially those entering the world of data science and machine learning.
If you want to learn more about NLP, take a look at: Must read NLP articles from the last 12 months.
nisha arya is a data scientist, freelance technical writer, and community manager at KDnuggets. She is particularly interested in providing professional data science advice or tutorials and theory-based data science insights. She also wants to explore the different ways in which artificial intelligence can benefit the longevity of human life. A great student looking to expand her technological knowledge and writing skills, while she helps guide others.
