A framework for understanding NLP

This post offers a framework to understand NLP (Natural Language Processing) technology in terms of techniques and applications. GPT-3 is used as an example of NLP technology to show how the framework can be used. GPT-3 is a large language model (LLM) with 175 billion parameters. We begin with a brief history of NLP.

NLP history
NLP definition
NLP techniques – framework
NLP technology application (GPT-3 use case)
NLP resources

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NLP history

The historical development of natural language processing (NLP) can be divided into four phases:

The early years (1950s-1960s): Researchers made significant progress in developing algorithms for tasks such as parsing, translation, and question answering. However, many of the challenges of NLP were not yet fully understood, and the field was still in its infancy.
The “AI winter” (1970s-1980s): The early optimism of the NLP community was tempered by a period of setbacks and disappointments. Many of the early algorithms for NLP were not as successful as had been hoped, and the field entered a period of decline.
The “rebirth” of NLP (1990s-present): The 1990s saw a resurgence of interest in NLP, thanks to advances in machine learning and artificial intelligence. Researchers developed new algorithms that were more powerful and more accurate than those of the previous generation. NLP began to be used in a wider range of applications, such as machine translation, text summarization, and question answering.
The “deep learning” era (2010s-present): The most recent phase of NLP has been driven by the development of deep learning. Deep learning models have achieved state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering. NLP is now a mature field with a wide range of applications, and it is poised to continue to grow and evolve in the years to come.

Here are some of the key milestones in the history of NLP:

1957: The first paper on machine translation is published.
1958: The first parser is developed.
1966: The first question answering system is developed.
1973: The first statistical language model is developed.
1980: The first neural network-based language model is developed.
1990: The first large-scale machine translation system is developed.
2000: The first text summarization system is developed.
2010: The first question answering system that can answer open-ended questions is developed.
2020: The first large language model is developed.

NLP definition

Traditionally, NLP is the study of how computers can understand and process human language. NLP studies the structure and rules of language and creates intelligent systems capable of deriving meaning from text and speech.

The goal of NLP is a computer capable of understanding the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

NLP uses ML techniques to analyze written text data. Broadly, NLP techniques are applied either for text understanding or text generation. Text understanding applications can be thought of as applications in text analysis – sometimes referred to as text mining, text data mining (TDM), and text analytics. Text generation can be thought of as applications in text summarization or text synthesis.

NLP techniques – framework

*ML methods: rule based, statistical, neural networks, deep learning.

*ML approaches: supervised, unsupervised, semi-supervised learning.

*ML algorithms: linear regression, logistic regression, K-means clustering, etc.

*NLP tasks (examples):

Natural language understanding (NLU): sentiment analysis, topic modeling, text classification, named entity recognition, document classification, content analysis.

Natural language generation (NLG): text summarization, text generation, code generation, question answering (information retrieval), translation, text to speech synthesis, chatbots.

*Technology application (use cases):

Sentiment analysis (NLU) in audience analysis in marketing.
NLG is used to generate a wide variety of content, including blog posts, articles, and even creative writing, e.g., in content marketing.
NLP is used in the defence industry/national security: Natural Language Processing: Security- and Defense-Related Lessons Learned (2021)

ML methods

*Rule-based systems use a set of hand-crafted rules to classify text.

*Statistical models are based on the idea of probability. They assume that each word in a document is generated by a mixture of topics, and each topic is associated with a probability of being used in a particular document.

*Neural networks (NNs) are a type of ML algorithm that is inspired by the human brain. They are made up of layers of interconnected nodes, and each node can learn to perform a specific task. Neural networks can learn complex patterns in data.

Neural networks are used to train sentiment analysis models, which can classify text as positive, negative, or neutral. Neural networks are also used to train machine translation models, which can translate text from one language to another. Machine translation models are used in a variety of applications, such as online translation tools and voice assistants.

Neural networks can be used for both supervised and unsupervised learning.

*Deep learning (DL) is a machine learning (ML) technique that uses neural networks with multiple layers.

Deep learning models have been shown to be very effective for NLP tasks that require complex pattern recognition, such as language modeling and question answering.

Deep learning is used to train language models, which are statistical models that can predict the next word in a sequence. Language models are used in a variety of NLP tasks, such as speech recognition, machine translation, and text generation.

Data requirements: deep learning requires more data to train than neural networks.

Deep learning can be used for both supervised and unsupervised learning.

ML approaches

Supervised learning is a type of ML where the model is trained on labeled data. The labels provide the model with information about the desired output. For example, if you are training a model to classify images of cats and dogs, the labels would tell the model whether each image is a cat or a dog.

Supervised learning provides the model with more information about the desired output than does unsupervised learning.

Supervised learning

Neural networks are used to train speech recognition models, which can convert speech into text.
Deep learning is used to train image classification models, which can classify images of objects, such as cats, dogs, and cars.

Unsupervised learning is a type of ML where the model is trained on unlabeled data. The model learns to identify patterns in the data without any guidance from labels. For example, if you are training a model to cluster images of animals, the model would learn to identify groups of images that are similar to each other.

Unsupervised learning

Neural networks are used to train recommender systems, which can recommend products or services to users based on their past behavior.
Deep learning is used to train image clustering models, which can group images of objects together based on their similarity.

Unsupervised ML does not require prior knowledge of the topics in the documents. Instead, it uses statistical methods or ML algorithms/techniques (black box reasoning) to find patterns in the words that are used in the documents.

ML algorithms

The three most common ML algorithms used in supervised learning are:

Linear regression: Linear regression is a simple but powerful algorithm that can be used to predict a continuous value from a set of features. It works by finding a line that best fits the data, and then using that line to make predictions.
Logistic regression: Logistic regression is a type of regression that is used to predict a categorical value, such as whether or not someone will buy a product. It works by finding a line that best separates the data into two groups, and then using that line to make predictions.
Decision trees: Decision trees are a type of supervised learning algorithm that can be used to make predictions based on a set of rules. They work by splitting the data into smaller and smaller groups, until each group can be classified with a high degree of certainty.

The three most common ML algorithms used in unsupervised learning are:

K-means clustering: K-means clustering is a simple but powerful algorithm that can be used to group data points into k clusters. It works by finding the k centroids, which are the points that are closest to the center of each cluster. The data points are then assigned to the cluster with the closest centroid.
Principal component analysis (PCA): PCA is a dimensionality reduction algorithm that can be used to reduce the number of features in a dataset. It works by finding the principal components, which are the directions in which the data varies the most. The data is then projected onto the principal components, which reduces the number of features.
Anomaly detection: Anomaly detection is a type of unsupervised learning that can be used to identify data points that are unusual or unexpected. It works by finding data points that are outside of the normal range of values.

NLP technology application (GPT-3 use case)

Our technology example is GPT-3, the third generation Generative Pre-trained Transformer. NLP is used in generating informational content for blog posts or YouTube videos. The two NLP tasks involved in this use case are text summarization and text generation.

Text summarization

A computer creates automatically an abstract or summary from an original man-made source text. Text summarization is the process of generating a shorter version of a text document that retains the most important information. NLG is often used to generate summaries of news articles, research papers, and other long documents.

How to do text summarization with deep learning and Python

Most of the current text summarization methods based on the deep learning method are supervised methods which need large-scale datasets. Text summarization techniques can be extractive or abstractive.

Six Unsupervised Extractive Text Summarization Techniques Side by Side

Text generation

GPT-3 is a deep learning ML model trained using data from the Internet to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text.

GPT-3 employs a combination of supervised and unsupervised learning methods. GPT-3 is capable of meta-learning, i.e., learning without any training. GPT-3 learning corpus consists of the common-crawl dataset. The dataset includes 45TB of textual data or most of the internet. GPT-3 is 175 Billion parameter models as compared to 10–100 Trillion parameters in a human brain.

GPT-3 is implemented in Python. The OpenAI API, which allows you to access GPT-3, is a Python library. There are also a number of Python libraries that allow you to fine-tune GPT-3.

GPT-3 uses a variety of ML algorithms, including:

Transformers: Transformers are a type of neural network that are well-suited for natural language processing tasks. They work by learning to predict the next word in a sequence, given the previous words.
Attention: Attention is a mechanism that allows transformers to focus on specific parts of the input sequence when making predictions. This makes them more accurate than other types of neural networks that do not use attention.
Generative pre-training: GPT-3 is trained using a process called generative pre-training. This means that it is first trained to predict the next word in a sequence, and then fine-tuned on a specific task. This makes it more generalizable than other types of ML models that are only trained on a specific task.

GPT-3 is a powerful language model that can be used for a variety of tasks, including:

Text generation: GPT-3 can be used to generate text, such as poems, code, scripts, musical pieces, email, letters, etc.
Translation: GPT-3 can be used to translate text from one language to another.
Question answering: GPT-3 can be used to answer questions about a given topic.
Summarization: GPT-3 can be used to summarize a given text.
Code generation: GPT-3 can be used to generate code, such as Python, Java, C++, etc.

NLP resources

Introduction to data science projects:

Natural Language Processing in Python (YouTube video)

Sentiment analysis, topic modeling, and text generation.

How to deal with NLP text data:

Data science workflow – the order of steps we take to solve a problem:

1) Start with the question (if I study more will I get a higher grade?)

2) Get and clean the data

3) Perform exploratory data analysis

4) Apply some NLP techniques / modeling

5) Share insights.

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