What is natural language processing?

Natural language processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It teaches machines to understand, interpret, and generate human language in a meaningful and useful way.

NLP is a booming area of AI; according to a report by Grand View Research, the global NLP market “was valued at USD 27.73 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) of 40.4% from 2023 to 2030.”

NLP combines computer science, linguistics, and machine learning to connect human communication and computer understanding.

NLP encompasses both natural language understanding, which interprets human language input, and natural language generation, which produces human-readable text output.

NLP technologies analyze and interpret written and spoken language, processing large volumes of text data efficiently. These systems power applications such as virtual assistants, chatbots, sentiment analysis tools, and machine translation platforms.

Advancements in NLP, such as the work of Mikolov et al., have significantly improved these systems' ability to understand language nuances and capture semantic relationships.

Main components of NLP

NLP involves the training of artificial intelligence systems on the following language components:

• Syntax refers to the arrangement of words and phrases to create well-formed sentences. NLP models use syntactic analysis to understand the grammatical structure of sentences and identify parts of speech and their relationships within a sentence.
• Semantics deals with the meaning of words and sentences. It helps NLP systems grasp the context and intent behind the text, rather than just processing the literal words. This component distinguishes between different meanings of a word based on context.
• Pragmatics focuses on the use of language in context and the interpretation of meaning in specific situations. It involves understanding the intent behind the communication and considering factors such as tone, sarcasm, and cultural nuances.
• Lexical analysis involves tasks such as speech tagging and entity recognition, which help identify parts of speech and named entities in text.

How does NLP work?

The process of NLP involves four steps to convert raw text into meaningful data:

1. Text preprocessing cleans and prepares the text for analysis. It removes irrelevant information, corrects misspellings, and standardizes formats.
2. Tokenization splits the text into individual units, such as words or phrases. Breaking down sentences into tokens lets NLP models analyze and process each component separately. “Word tokenization is more complex in languages like written Chinese, Japanese, and Thai, which do not use spaces to mark potential word boundaries.” (Stanford University study)
3. Parsing analyzes the syntactic structure of the text. It identifies the grammatical relationships between words, creating a parse tree that represents the sentence's structure. Parsing aids in understanding how different parts of a sentence relate to each other.
4. Semantic analysis focuses on understanding the meaning of the text. It interprets the relationships between words and phrases to grasp the overall context and intent. Semantic analysis helps distinguish between different meanings of words based on their usage in sentences.

NLP employs different techniques and methods to analyze and understand human language. These approaches form the foundation of modern NLP systems and applications.

1. Rule-based approaches
2. Statistical methods
3. Machine learning
4. Deep learning

Rule-based approaches

Early NLP systems relied on handcrafted linguistic rules created by experts. These rules specified patterns and relationships between words to interpret and generate language. While effective for simple tasks, rule-based systems struggled with the complexity and variability of natural language.

Statistical methods

Statistical methods use large datasets to train models that recognize patterns and make predictions based on probabilities. N-grams and hidden Markov models (HMMs) analyze word sequences to predict likely word combinations. Statistical methods improved accuracy and adaptability compared to rule-based approaches but required substantial amounts of data.

Machine learning in NLP

Machine learning revolutionized NLP with systems that learn from data without explicit programming. Supervised learning algorithms, such as decision trees and support vector machines (SVMs), train models on labeled datasets. These models then classify text, recognize named entities, or perform sentiment analysis.

Unsupervised learning, such as clustering and topic modeling, identifies patterns and structures in unlabeled data. Advanced machine learning methods and deep learning techniques have revolutionized NLP, enabling the creation of sophisticated language models that can understand and generate human-like text.

Deep learning and NLP

Deep learning, a subset of machine learning, drove significant advancements in NLP. Neural networks, particularly deep neural networks, learn hierarchical representations of data, allowing them to capture complex patterns in language.

Deep learning foundation models leverage transfer learning, where models are pre-trained on large datasets and then fine-tuned for specific tasks. This approach has dramatically improved performance across NLP applications. For instance, word embeddings like Word2Vec and GloVe capture semantic relationships between words for nuanced language understanding. These pre-trained embeddings serve as building blocks for more complex NLP tasks, such as sentiment analysis, named entity recognition, and text classification.

Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks process sequential data effectively. These architectures perform well in tasks such as language modeling and machine translation because of their ability to handle time-dependent information. While newer architectures have emerged, RNNs and LSTMs remain valuable for certain NLP applications, particularly those involving time-series data or where computational resources are limited.

Transformers and attention mechanisms

Transformers represent the latest breakthrough in NLP. Unlike RNNs, transformers process entire sentences at once, capturing long-range dependencies more effectively. The attention mechanism within transformers focuses on relevant parts of the input text, improving performance on tasks such as translation and text generation.

In their report “Attention Is All You Need,” a team of researchers demonstrated the superiority of the transformer to previous state-of-the-art models for machine translation tasks, at a fraction of the training cost.

Bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT) are notable transformer-based models that set new benchmarks in NLP. These also underpin the recent surge in generative AI products such as ChatGPT and Claude.