Comprehensive Guide to NLP [2023 Edition]
Guide to Natural Language Processing, an Introduction to NLP [2023 Edition]
Guide to Natural Language Processing, an Introduction to NLP [2023 Edition]
Natural Language Processing (NLP) is an essential composent of Machine Learning applications today. In this guide, we'll walk you through the components of an NLP stack and its business applications.
Table of Contents
Natural Language Processing (NLP) is an interdisciplinary field focusing on the interaction between humans and computers using natural language. With the increasing amounts of text-based data being generated every day, NLP has become an essential tool in the field of data science. In this blog, we will dive into the basics of NLP, how it works, its history and research, different NLP tasks, including the rise of large language models (LLMs), and the application areas.
Natural Language Processing, what is it, again?
NLP is a subfield of artificial intelligence that deals with the processing and analysis of human language. It aims to enable machines to understand, interpret, and generate human language, just as humans do. This includes everything from simple text analysis and classification to advanced language modeling, natural language understanding (NLU), and generation (NLG).
NLU involves developing algorithms and models to analyze and interpret human language, including spoken language and written text. The goal of NLU is to enable machines to understand the meaning of human language by identifying the entities, concepts, relationships, and intents expressed in a piece of text or speech. Some common tasks in NLU include sentiment analysis, named entity recognition, semantic parsing, and machine translation.
NLG involves developing algorithms and models to generate human-like language, typically responding to some input or query. The goal of NLG is to enable machines to produce text that is fluent, coherent, and informative by selecting and organizing words, phrases, and sentences in a way that conveys a specific message or idea. Some common tasks in NLG include text summarization, dialogue generation, and language translation.
To summarize, NLU is about understanding human language, while NLG is about generating human-like language. Both areas are important for building intelligent conversational agents, chatbots, and other NLP applications that interact with humans naturally.
How does NLP work?
NLP techniques can be divided into two broad categories: rule-based systems and machine-learning systems. Rule-based systems rely on pre-defined rules and linguistic knowledge to process text, while machine learning systems use statistical algorithms and models to learn patterns from data and improve their accuracy over time.
NLP works by breaking down language into its smallest units (words, phrases, sentences) and analyzing the structure and meaning of those units. It can involve different steps:
Tokenization
Tokenization is the process of breaking down a piece of text into individual words or phrases, known as tokens. This is typically the first step in NLP, as it allows the computer to analyze and understand the structure of the text. For example, the sentence "The cat sat on the mat" would be tokenized into the tokens "The", "cat", "sat", "on", "the", and "mat".
Part-of-speech tagging
Part-of-speech tagging is the process of identifying the grammatical structure of each word or phrase in a piece of text. This involves labeling each token with its part of speech, such as noun, verb, adjective, etc. This information is important for understanding the meaning of the text, as different parts of speech can convey different meanings. For example, in the sentence: "The cat sat on the mat", "cat" is a noun, "sat" is a verb, and "the" is a determiner.
Syntactic analysis
Syntactic analysis, also known as parsing, is the process of analyzing the grammatical structure of a sentence to identify its constituent parts and how they relate to each other. This involves identifying the different parts of speech in a sentence and understanding the relationships between them. For example, in the sentence "The cat sat on the mat", the syntactic analysis would involve identifying "cat" as the subject of the sentence and "sat" as the verb.
Semantic analysis
Semantic analysis is the process of understanding the meaning of a piece of text beyond just its grammatical structure. This involves analyzing the relationships between words and phrases in a sentence to infer meaning. For example, in the sentence "I need to buy a new car", the semantic analysis would involve understanding that "buy" means to purchase and that "car" refers to a mode of transportation.
These techniques are all used in different stages of NLP to help computers understand and interpret human language.
Natural Language Processing History and Research
NLP has a long history, dating back to the 1950s. The early years were focused on rule-based systems and symbolic methods, such as Chomsky’s generative grammar, that aimed to represent language using formal rules. In the 1980s and 90s, machine learning methods gained popularity, introducing statistical models such as Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs). More recently, the development of deep learning and neural networks has revolutionized NLP, leading to the creation of large language models (LLMs) such as BERT, GPT, and T5, which we will explore further in section 6.
NLP tasks & models
NLP encompasses a wide range of tasks, including but not limited to:
Speech recognition: converting audio speech into text
Named Entity Recognition (NER): identifying and categorizing entities such as people, organizations, and locations in a text
Sentiment analysis: identifying the tone or sentiment behind a piece of text
Machine translation: translating text from one language to another
Text classification: categorizing text into predefined categories
Question answering: answering natural language questions posed by users
Text summarization: generating a summary of a longer piece of text
These tasks can be achieved by different models types; here are the most common ones:
Rule-based models: Rule-based models use a set of pre-defined rules to analyze and process text data. These rules can be based on grammar, syntax, or other linguistic rules. Tasks associated with rule-based models include tokenization, part-of-speech tagging, and named entity recognition.
Statistical models: Statistical models use probabilistic algorithms to analyze and process text data. These models are often based on machine learning algorithms such as decision trees, support vector machines, and hidden Markov models. Tasks associated with statistical models include sentiment analysis, topic modeling, and machine translation.
Neural network models: Neural network models use deep learning algorithms to analyze and process text data. These models are often based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models. Tasks associated with neural network models include natural language generation, question answering, and text classification.
Transformer models: Transformer models are a type of neural network model that has become increasingly popular recently. They are designed to process sequential data, such as text, by creating attention mechanisms that allow the model to focus on different parts of the sequence. Tasks associated with transformer models include language modeling, machine translation, and summarization.
Overall, each model type has its strengths and weaknesses, and the best model for a particular task will depend on factors such as the amount and type of data available, the complexity of the task, and the desired level of accuracy.
Focus on Large Language Models (LLMs) in NLP
LLMs are a type of machine learning model that uses deep neural networks to learn from vast amounts of text data. These models have transformed NLP, allowing for more accurate and efficient language processing, and have been at the forefront of recent breakthroughs in NLP research.
The principle behind LLMs is to pre-train a language model on large amounts of text data, such as Wikipedia, and then fine-tune the model on a smaller, task-specific dataset. This approach has proven to be highly effective, achieving state-of-the-art performance on many NLP tasks.
Training an LLM requires a large amount of labeled data, which can be a time-consuming and expensive process. One way to mitigate this is by using the LLM as a labeling copilot to generate data to train smaller models. This approach has been used successfully in various applications, such as text classification and named entity recognition.
Natural Language Processing applications - why is it important?
NLP has numerous applications in various industries, including healthcare, finance, marketing, and customer service. Here are some of the key NLP use cases:
Chatbots
Chatbots are virtual assistants that use NLP to understand natural language and respond to user queries in a human-like manner. They can be used for customer service, sales, and support and have become increasingly popular recently. For example, a chatbot can help a customer book a flight, find a product, or get technical support.
Spam detection
NLP can be used to identify and filter spam emails or messages. By analyzing the text of an email or message, NLP algorithms can identify spam keywords or patterns and flag them for further action.
Text summarization
NLP can be used to automatically summarize long documents or articles into shorter, more concise versions. This can be useful for news aggregation, research papers, or legal documents.
Sentiment analysis
NLP can be used to analyze the sentiment or emotion behind a piece of text, such as a customer review or social media post. This information can be used to gauge public opinion or to improve customer service.
Named Entity Recognition (NER)
NER is used to identify and extract named entities such as people, organizations, and locations from text data. This can be used for various applications such as social media monitoring, news analysis, and fraud detection.
Machine translation
NLP can be used to translate text from one language to another. This has numerous applications in international business, diplomacy, and education.
These NLP applications can be illustrated with examples using Kili Technology, a data annotation platform that allows users to label data for machine learning models. For example, to train a chatbot, users can annotate customer messages and responses using Kili, providing the data necessary to train the model to understand natural language and respond to customer queries.
Valuable NLP material: our recommendations
Natural Language Processing Datasets
On Kili Technology's GitHub, you can find a list of awesome datasets on NLP. They are split across tasks, languages,s and sectors, like the CCCS-CIC-AndMal-2020, which proposes a new comprehensive and huge android malware dataset, named CCCS-CIC-AndMal-2020 including 200K benign and 200K malware samples for a total of 400K android apps with 14 prominent malware categories and 191 eminent malware families.
In the Finance sector, SEC-filings is generated using CoNll2003 data and financial documents obtained from U.S. Security and Exchange Commission (SEC) filings.
In French on the medical sector, QUAERO French Medical Corpus was initially developed as a resource for named entity recognition and normalization.
Natural Language Processing: State-of-the-art Papers
To further improve NLP skills, various ressources are relevant:
NLP-Overview provides a current overview of deep learning techniques applied to NLP, including theory, implementations, applications, and state-of-the-art results. It's an excellent resource for researchers new to Deep NLP.
NLP-Progress tracks the advancements in Natural Language Processing, including datasets and the current state-of-the-art for the most common NLP tasks. The article "NLP's ImageNet moment has arrived" discusses the recent emergence of large pre-trained language models as a significant advancement in the field of NLP.
ACL 2018 Highlights: Understanding Representation and Evaluation in More Challenging Settings offers insights on the latest advancements in NLP, with a focus on representation and evaluation in challenging settings.
Four deep learning trends from ACL 2017. Part One: Linguistic Structure and Word Embeddings