How Does Natural Language Processing Work?

NLP is a rapidly growing branch of AI that enables computers to interpret and interact with human language. By training machines to interpret, analyze, and respond to human text and speech, NLP can transform professional industries and assist in daily interactions.

NLP is all around you: customer service chatbots that understand and respond to queries; health care tools that personalize patient care, analyze patient data, and assist physicians with diagnoses; and cyber threat detection systems that identify phishing attempts based on irregular text patterns. But how does natural language processing work? Learn more about NLP’s fundamentals, processes, and applications to help you determine the answer.

Understanding the fundamentals of NLP

NLP is the study and development of systems that train computers to understand and interpret human language. By combining elements of linguistics, computer science, and artificial intelligence, NLP teaches machines to comprehend and respond to the complexities of human communication.

Key components of NLP include:

• Syntax: The grammatical structure of sentences

• Semantics: The meaning of words and sentences

• Pragmatics: The varied meaning of words in specific contexts

By integrating these components, NLP systems can perform tasks such as translating languages, summarizing documents, and answering queries.

How do you “do” natural language processing?

NLP models follow several important steps, including text preprocessing, syntax analysis, and semantic analysis, to process and transform raw language into meaningful outputs.

Text preprocessing

NLP models preprocess the input text before analyzing it using several techniques to clean and prepare it. This might include:

• Tokenization: Breaking down text into smaller words or phrases for faster, easier processing

• Stop-word removal: Eliminating common words that do not add significant meaning, like “the” or “is.” 

• Stemming and lemmatization: Reducing words to their base or root form, such as changing “running” into the more straightforward “run.”

Syntax analysis

NLP models conduct syntax analysis through various methods to understand a sentence's grammatical structure.

• Part-of-speech tagging: Categorizing words into parts of speech, such as nouns, verbs, and adjectives

• Parsing: Mapping relationships between words in a sentence to understand their syntactic roles

Semantic analysis

After an NLP system grasps the text's literal meaning through preprocessing and syntax analysis, it uses various techniques to conduct semantic analysis to derive meaning from the text's nuances and implications.

• Word embeddings: Representing words as vectors in a multidimensional space to capture their relationships

• Context understanding: Leveraging models like Bidirectional Encoder Representations from Transformers (BERT) to interpret words based on their surrounding context

Natural language processing techniques 

NLP systems rely on various techniques and algorithms to quickly and accurately perform complex language-related tasks, including machine learning, deep learning, and natural language generation.

Machine learning methods

• Supervised learning: Training models on labeled data for specific tasks like sentiment analysis

• Unsupervised learning: Extracting patterns from unlabeled data, such as clustering similar words or other syntax analysis methods

Deep learning techniques

• Recurrent neural networks (RNNs): Handle tasks like text generation and speech recognition in sequential data by capturing dependencies between words and characters in text

• Transformers: Models such as ChatGPT and BERT use attention mechanisms to understand context across long sequences to translate or summarize text

Natural language generation

Natural language generation (NLG) focuses on enabling machines to create human-like text. It powers applications such as automated context writing, personalized email responses, and storytelling tools.

Challenges in NLP

Despite many great strides, NLP still faces key challenges related to language ambiguity, language diversity, and contextual understanding. 

• Ambiguity in language: Human language is inherently ambiguous. Polysemy, which refers to words with multiple meanings, and homonymy, which refers to words that sound the same and are spelled the same but have different meanings, are particularly challenging for NLP models.

• Contextual understanding: Understanding the nuances of context is challenging for NLP models. It requires the machine to learn how to distinguish between words, their possible meanings, and how their different definitions might alter a sentence’s context.

• Different languages and dialects: Processing multiple languages, regional variations, and dialects, as well as ensuring accuracy across linguistic diversity, is challenging for NLP models. NLP models that use more extensive and diverse data sets are more capable of handling these issues. 

Natural language processing examples

Despite its challenges, NLP still has a broad range of applications across numerous industries, including:

• Chatbots and virtual assistants: NLP powers customer service tools like chatbots and virtual assistants such as Alexa or Siri. These systems understand queries, provide answers, and carry out tasks like booking appointments.

• Sentiment analysis: By analyzing opinions and emotions in text, NLP helps gauge customer feedback, monitor social media sentiment, and understand public opinion.

• Machine translation: Tools like Google Translate rely on NLP to convert text from one language to another, helping the system overcome linguistic barriers.

NLP Applications

Various industries and professionals are leveraging NLP technology, including:

• Technology companies: Developing NLP-powered search engines and voice assistants

• Healthcare providers: Analyzing patient data for diagnoses and treatment options

• Financial institutions: Detecting fraud and analyzing market trends

• Data scientists: Building and refining NLP models for custom use cases

• Machine learning engineers: Improving NLP systems’ autonomy and performance

• Software developers: Integrating NLP into new applications and platforms

Future trends in NLP

As NLP continues to evolve, several major trends are shaping its future. Conversational AI is advancing rapidly, and chatbots and voice assistants are becoming increasingly refined. This increasing sophistication will enable more natural, human-like interactions with machines.

NLP also plays a growing role in augmented reality (AR) and the Internet of Things (IoT). As NLP becomes more prevalent in emerging technologies, interactions between humans and computers will continue to improve.

Learn more about NLP with Coursera

NLP bridges human communication and machine understanding through the use of advanced algorithms and AI techniques. It helps systems process, analyze, and respond accurately and intelligently. Learn more about this rapidly growing field on Coursera.

Consider the Machine Learning Specialization from Stanford and DeepLearning.AI on Coursera if you're just starting. This three-course series explains the fundamental concepts of AI and the basics of machine learning.