Natural Language Processing (NLP) is where artificial intelligence (AI) meets linguistics, focusing on how computers interact with human language. In simple terms, NLP is about enabling a computer program to understand what we say and write. Although it has existed for over 50 years (Gillis et al., 2024), it’s only recently that we’ve started seeing its real-world uses more often. Take Chat GPT, for example many of us use it regularly. But how exactly does NLP work, and where else is it applied? This article will delve into these questions.
How does NLP work?
Enabling computers to understand human language usually involves two main steps: data preprocessing and algorithm development.
Data preprocessing compromises of transformation of the text in order to make it more manageable for the computer. This includes several steps such as:
- Tokenization: converting a text into smaller parts like individual words and phrases, which are further known as tokens. (Natural language processing (NLP): What is it & how does it work?)
- Stop word removal: eliminating common words e.g. ‘a’, ‘an’, ‘the’ that don’t add significant meaning to the sentence. (What is Natural Language Processing (NLP) & How Does It Work?)
- Stemming and lemmatization: reducing words to their root forms. For example, in the sentence: ‘The boy is playing’, the verb ‘playing’ would be reduced to ‘play’, and the verb ‘is’ would be reduced to ‘be’. (What is Natural Language Processing (NLP) & How Does It Work?)
Once the data is preprocessed, an algorithm is developed to process the text. Two types of algorithms are mostly used: rule-based algorithms and machine learning approaches.
Rule based approach
Rule based approach uses carefully designed linguistic rules to process language (Gillis et al., 2024). For instance:
- Reliance on hand-crafted rules created by linguists and domain experts.
- Use of lexicons, grammars.
- Semantic frameworks and deterministic outcomes based on predefined rules.
This approach is usually used with simple chatbots with predefined responses and basic named entity recognition systems.
Machine Learning algorithms
Machine learning algorithms use statistical methods to learn from data and adjust their approach based on the patterns observed in training data (Gillis et al., 2024). This category can be further divided into: traditional ML, deep learning and neural networks. The advantages of ML approach include:
- Ability to work with large amounts of data and learn from it.
- Ability to easily adapt to new patterns and languages.
- Better at understanding language in a more nuanced way compared to rule-based systems.
Examples of machine learning-based NLP applications include advanced chatbots and virtual assistants (e.g., GPT-based models) and sentiment analysis tools. It’s worth noting that a hybrid approach is often used, combining rule-based and ML algorithms.
Algorithm development is crucial in syntactic and semantic analysis. These steps involve creating or applying algorithms to handle data and extract meaning from it.
Syntactic analysis focuses on the analysis of the structure of sentences. For example:
- Part-of-speech tagging: identifying words as nouns, verbs, adjectives, etc. (Natural language processing (NLP): What is it & how does it work?)
- Parsing: building parse trees that show the grammatical structure of sentences (Natural language processing (NLP) – A complete guide)
Semantic analysis, on the other hand, digs into the meaning of the text. Some key tasks include:
- Named entity recognition: identifying and classifying named entities such as people, places, organizations.
- Relationship extraction: finding connections between entities in the text
- Sentiment analysis: determining the emotional tone of the text.
Finally, NLP systems produce results based on the specific task, like translated text, summaries, answers to questions, or even generating new text.
Practical Applications of NLP in the Real World
- Customer Feedback Analysis: NLP helps businesses analyze large volumes of customer feedback from various sources like reviews, surveys, and social media. This in turn enables companies to extract valuable insights, identify trends, and improve their products or services based on customer sentiment and opinions. One of the most widely used methods for customer feedback analysis is sentiment analysis. For more read, X.
- Customer Service Automation: As mentioned previously, chatbots and virtual assistants based on NLP can understand and respond to human language. This gives rise to many advantages such as: 24/7 support, reduced response times, and a high volume of customer interactions held efficiently.
- Spam Detection: Have you ever wondered how some of your mails are spotted as spam and others are not? Simply, NLP algorithms are used to identify and filter out spam messages, ensuring that users’ inboxes are kept free from unwanted emails.
- Smart Assistants: Virtual assistants such as Alexa or Siri through the use of NLP understand and respond to human language. By interpreting and acting on natural language, they perform tasks, answer questions or control smart home devices.
- Predictive Text: NLP algorithms drive predictive text and auto-complete features in smartphones, search engines, and word processors. These algorithms suggest words or phrases as you type, making communication faster and more efficient. One of the examples can be seen below.
To sum up, NLP allows computers to understand and interact with human language, revolutionizing various industries. From customer feedback analysis and service automation to spam detection, smart assistants, and predictive text, NLP’s applications are vast and continually expanding, enhancing our daily interactions with technology. And there’s still so much more to come.
References:
Gillis, A.S., Lutkevich, B. and Burns, E. (2024) What is natural language processing?: Definition from TechTarget, Enterprise AI. Available at: https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP (Accessed: 08 July 2024).
Natural language processing (NLP): What is it & how does it work? (no date) MonkeyLearn. Available at: https://monkeylearn.com/natural-language-processing/ (Accessed: 08 July 2024).
What is Natural Language Processing (NLP) & How Does It Work? (no date) RSS. Available at: https://levity.ai/blog/how-natural-language-processing-works (Accessed: 08 July 2024). Natural language processing (NLP) – A complete guide (no date) (NLP) [A Complete Guide]. Available at: https://www.deeplearning.ai/resources/natural-language-processing/ (Accessed: 08 July 2024).
