The Rapid Growth of NLP

NLP is a relatively new field that is advancing rapidly. New announcements for advancements happen almost daily. For example, in just the past fortnight Amazon have announced the release of their AlexaTM. This is a new 20B language model with significant improvements in summarisation and translation. OpenAI has released Whisper – a new open-source machine learning model for multi-lingual automatic speech recognition.

How Can Natural Language Processing Help Business?

Here are four things that computers are now very good at, which most businesses do not know about. These areas offer significant efficiencies and opportunities to be realised immediately:

1. Translating text from one language to another

Computers can now do this almost as well as humans and orders of magnitude more efficiently. Google translate is already obsolete. Any business that produces text in multiple languages should be looking at how technology can do this cheaper, quicker and better. Amazon’s AlexaTM 20B language model is just the latest advance in this space.

2. Writing compelling, persuasive business prose quickly and cheaply

Businesses that write blogs, tenders, proposals or marketing copy can benefit from using modern NLP capabilities. NLP can now produce high-quality content more quickly and more efficiently.

No one would create a financial model without using Excel. In the very near future, no-one will write professional prose without using AI writing support. This technology is here now but has not yet seen wide adoption. My own business, AutogenAI, is building enterprise-level text production solutions in exactly this space.

3. Transcribing from human speech more quickly and more accurately than most human transcribers

Automating speech-to-text can unlock the potential for businesses to save money on expensive transcription services and make it possible to transcribe far more spoken content. For example, it is now possible and economical to capture and efficiently share everything that is said at all company meetings across the globe.

4. Understanding and categorising human language

This has applications across most businesses. For example, tech can help to understand customer reviews and social media posts. This can identify trends in customer sentiment and spot opportunities for improvement. Alternatively it can simply better understand how customers feel about a product or service.

A Brief History of Natural Language Processing

The history of Natural Language Processing (NLP) spans several decades and has evolved significantly over time. Here’s a brief overview of its development:

1950s-1960s: The Origins

The field of NLP can be traced back to the 1950s with the advent of computers and the rise of artificial intelligence. Early efforts focused on translating languages using machine-based approaches.

One of the first significant contributions was the Georgetown-IBM machine translation system, which translated Russian sentences into English. It laid the groundwork for later NLP research.

1970s-1980s: Rule-Based Systems

During this period, researchers began developing rule-based systems for NLP. These systems relied on explicit linguistic rules and hand-crafted grammars to process and generate text.

Projects like SHRDLU, a natural language understanding system developed at MIT, demonstrated the potential of NLP for human-computer interaction.

However, rule-based systems had limitations, particularly by the complexity of language. As well the difficulty of manually encoding all linguistic rules.

1990s-2000s: Statistical Methods and Corpora

The shift towards statistical approaches marked a turning point in NLP. Researchers began using a large corpora of text to train models and extract patterns.

The introduction of machine learning algorithms improved various tasks. Algorithms such as Hidden Markov Models (HMMs) and probabilistic context-free grammars (PCFGs) improved the accuracy of various NLP tasks. Tasks such as part-of-speech tagging and syntactic parsing.

The development of the Penn Treebank saw large advancements. Large annotated corpus of English text, contributed to the advancement of syntactic and semantic parsing.

2000s-Present: Deep Learning and Neural Networks

The mid-2000s saw a resurgence of interest in neural networks, leading to the development of deep learning techniques for NLP.

Word embeddings (e.g., Word2Vec, GloVe) allowed words to be represented as dense vectors in continuous vector spaces. Capturing semantic relationships between words.

The introduction of recurrent neural networks (RNNs) and later, attention mechanisms and transformer architectures, revolutionised NLP. The Transformer architecture, introduced in the paper “Attention Is All You Need” in 2017.  This forms the basis of models like BERT, GPT, and many others.

Transfer learning became a central paradigm, where pre-trained language models could be fine-tuned for specific tasks. All leading to significant performance improvements across a wide range of NLP tasks.

Recent Years: Advances and Applications

Recent years have witnessed rapid progress in NLP. With models like GPT-3 achieving remarkable language generation capabilities and BERT achieving state-of-the-art results on various benchmarks.

NLP applications have proliferated, including machine translation, sentiment analysis, question answering, chatbots, and more.

Research efforts continue to focus on improving model robustness, interpretability, and addressing biases in language models.

Overall, the history of NLP is marked by the evolution from rule-based systems to data-driven approaches. All culminating in the widespread adoption of deep learning techniques. As well as pre-trained language models that have transformed the field’s capabilities and applications.

Summarising long pieces of text

Summarising long texts has always been difficult for NLP. However, recent advancements have catapulted past this issue. It is possible for a computer to read a long document and produce a shorter summary that captures the main points.

This is applicable across almost all business areas and functions. For example, a computer can read through a large number of legal documents and produce a summary of the key points.

Language technology is going to transform the world over the next decade. We want to demonstrate what it can deliver now, which you can find on our case studies page.