- Natural Language Processing (NLP) stands at the forefront of cutting-edge technology, empowering machines to understand, interpret, and generate human language.
- NLP is a subfield of linguistics, computer science, and artificial intelligence that uses 5 NLP processing steps to gain insights from large volumes of text—without needing to process it all.
- Natural language processing consists of 5 steps machines follow to analyse, categorise, and understand spoken and written language. The 5 steps of NLP rely on deep neural network-style machine learning to mimic the brain’s capacity to learn and process data correctly.
Natural Language Processing is a dynamic and evolving field with widespread applications across various industries. By understanding the five key steps outlined in this blog—tokenisation, text cleaning, feature extraction, modeling, and evaluation—developers and data scientists can leverage the power of NLP to unlock valuable insights from textual data, driving innovation and advancement in our digital world. This article explores these fundamental NLP steps and how leveraging NLP in business applications can enhance customer interactions within your organisation.
Also read: Exploring the best conversational AI platforms
What is NLP?
Natural language processing consists of 5 steps machines follow to analyse, categorise, and understand spoken and written language. The 5 steps of NLP rely on deep neural network-style machine learning to mimic the brain’s capacity to learn and process data correctly.
Businesses use tools and algorithms that follow the 5 NLP stages to gather insights from large data sets and make informed business decisions. Some NLP business applications include text-to-speech, chatbox, urgency detection, autocorrection, sentiment analysis, speech recognition, etc.
Also read: The difference between Conversational AI and GenAI
1. Tokenisation: Breaking down the text
The first step in NLP is tokenisation, where raw text is broken down into smaller units called tokens. These tokens can be words, phrases, or even individual characters, depending on the level of granularity required. Tokenisation lays the foundation for subsequent NLP tasks by segmenting the text into manageable units for analysis.
2. Text cleaning and preprocessing
Raw text often contains noise and inconsistencies that can hinder NLP tasks. Text cleaning and preprocessing involve removing irrelevant characters, punctuation, and formatting, as well as handling capitalisation and converting text to a standardised format. Techniques such as stemming and lemmatisation further refine the text by reducing words to their base or root forms, improving the efficiency and accuracy of downstream NLP tasks.
3. Feature extraction: Unveiling insights from text
Once the text is tokenised and preprocessed, the next step is feature extraction, where relevant information is extracted from the text to represent it in a numerical format suitable for machine learning algorithms. Common feature extraction techniques include bag-of-words, TF-IDF (Term Frequency-Inverse Document Frequency), and word embeddings like Word2Vec and GloVe. These techniques capture semantic relationships and contextual information within the text, enabling machines to understand and analyse language more effectively.
4. Modeling and analysis
With the text transformed into numerical features, it’s ready for modeling and analysis. This step involves applying various machine learning or deep learning algorithms to the processed text to perform tasks such as sentiment analysis, named entity recognition, topic modeling, and text classification. Supervised, unsupervised, and semi-supervised learning techniques are often employed, depending on the nature of the NLP task and the availability of labeled data.
5. Evaluation and iteration: Fine-tuning for optimal performance
The final step in NLP involves evaluating the performance of the models and iterating to improve their accuracy and efficiency. Metrics such as accuracy, precision, recall, and F1-score are commonly used to assess model performance. Feedback from real-world usage and domain experts is also valuable for refining and fine-tuning NLP models to meet specific requirements and achieve optimal performance.
