Natural Language Processing
Discover how natural language processing is enabling end-to-end process automation, process improvement, and unprecedented customer intelligence and business intelligence.
What is Natural Language Processing?
Natural Language Processing (NLP) is the application of machine learning to create value from human natural language. It describes a large number of different solutions, but most commonly NLP helps train machines to understand and correctly process text or speech.
NLP is often used in conjunction with other AI technologies to form a complete solution. It is present in everything from internet search engines to chat bots and speech recognition applications.
What does Natural Language mean?
Natural language is the freeform, often conversational language that humans use to communicate with each other. It is distinct from formal languages - such as programming language - as it has no hard internal rules that decide how the language must be conveyed or understood.
If you misplace or misuse a word in a normal conversation, this shouldn’t matter as long as the other person can infer what you meant. By contrast, if you make a mistake in a programming language this will almost always break the process or keep you from the outcome you wanted.
The human factor is key. Humans innately learn and understand language, and can use context or judgement to infer the intended meaning. Machines can’t do this without assistance, making NLP necessary to give them this understanding.
What is Natural Language Processing in AI and Machine Learning?
What is Natural Language Processing in AI?
Artificial intelligence (AI) is a very broad field that includes many different kinds of applications and algorithms. NLP is only one application, or domain, of AI. It can be used to describe any solution that uses AI technology to teach machines how to understand the natural language of humans.
What is Natural Language Processing in machine learning?
Machine learning plays a very important role in NLP. In fact, NLP could even be described as a type of machine learning - training machines to produce outcomes from natural language.
Machine learning is key to the process by which NLP models are trained to understand natural language. There are numerous different machine learning techniques used in NLP model training:
- Supervised Learning
Supervised learning is when an algorithm is trained on input data that has been labelled by humans for a particular output. The intention is to train a model so that it understands and interprets future data in a specific desired way.
- Semi-supervised Learning
Semi-supervised learning is used in situations where the input data set is only partially labelled. It is popular in real-world scenarios where data is almost never entirely classified.
- Unsupervised Learning
In unsupervised learning, a model is given unlabelled data as a training set. The objective is usually to train the algorithm to discover findings that humans otherwise would not notice or think to look for.
- Active Learning
Active learning describes the process by which machines and human annotators work collaboratively to train a model. The objective is to reduce the amount of time and data to get a machine learning model into production, while ensuring outcomes that are relevant and correct for a specific context.
How is Natural Language Processing different from Natural Language Understanding?
There are important distinctions between NLP and Natural Language Understanding (NLU). NLP is focused on the breaking down and processing of human natural language, while NLU is focussed on language comprehension - such as comprehending and understanding the meaning of a sentence or message.
NLP takes text and transforms it into smaller pieces - usually vectors of numbers - that are easier for computers to use. NLP identifies important entities and parts of speech and text. Meanwhile, NLU is more often used to understand the meaning behind a text and in tasks like summarising news articles, language detection, and serving up the right products for a search request.
In practice, many AI solutions will combine NLP and NLU technologies to enable them to understand meaning, make decisions and trigger actions based on inputs constructed from natural language.
How does Natural Language Processing work?
Every NLP solution is different and will use different techniques and algorithms to deliver the outcome that its users want. The most important factor in determining how a solution will work is the task or tasks it is given to perform.
However, there are important commonalities between different NLP solutions. As computers and machines lack the contextual and general awareness of humans, they don’t have the ability to understand human language innately. NLP gives them this understanding, and most solutions work by using specialised algorithms to break raw text into smaller chunks of words and sentences - known as tokens. These tokens are converted into sequences of numbers called embeddings, which are then fed into the NLP model. Other algorithms are then used to reason from and produce the intended outputs from these embeddings.
Watch an on-demand demo of Re:infer’s NLP-based Conversational Data Intelligence Platform.
Why is Natural Language Processing important?
Natural Language Processing is important because it provides a solution to one of the biggest challenges facing people and businesses - an overabundance of natural language information.
Communication has gone digital. Digital communication channels like email, text and instant messaging are rapidly overtaking all other forms of communication. This is beneficial as it removes what has always been the biggest barrier to human interaction - distance. With digital communications, you can have a conversation with almost anyone, almost anywhere and at any time of the day. This new reality has already transformed society and business.
However, one of the consequences of this has been vast amounts of natural language data, and the resulting challenge of how to process all of it. Email alone is growing exponentially - Almost 320 billion emails are sent every day, and this is expected to grow to over 376 billion by 2025. If you were to store every word ever spoken by humans, it would take up approximately 5 exabytes of data. But there’s more than 44 exabytes of data in the world today, with 80-90% of it likely being text data expressed in natural language.
Put simply, there is too much natural language information and not enough people to process it all. Where the speed and accuracy of response is important - such as in business and the public sector - this is causing serious problems. The slow manual processing of information causes delays, damages the customer experience, and in the worst cases causes complete process breakdown when messages fall through the cracks. It’s also important to consider the huge value of the information contained in natural language data. But if businesses have no efficient and reliable way of extracting it then it’s simply wasted potential.
The communications overload is also putting incredible pressure on workers, creating stress and constraining their productivity. The average employee sends or receives 126 emails a day, and up to 40% of their time is spent in Outlook alone. Nearly a third (30%) of workers see their inbox as the biggest distraction from actual work, and 22% say they want to quit their current role due to exploding email volumes.
NLP holds the potential to solve this challenge. By giving machines the ability to read and understand natural language, NLP enables businesses to rapidly analyse and extract the value from vast quantities of natural language data. NLP provides the capability to convert unstructured language into structured data, which also enables businesses to create automated intelligent workflows that free employees from repetitive information processing tasks.
What are the advantages of Natural Language Processing?
NLP gives businesses the capability to extract value from natural language data rapidly across the enterprise. When deployed across an organisation’s many communications channels and data environments, business leaders gain unprecedented insight into operations and the data needed to drive powerful new automations.
- Better business, customer and product intelligence
Conversations reveal a great deal of new insights into business challenges and opportunities, but only NLP makes them understandable and, therefore, actionable at scale.
- Faster, more responsive service
With NLP, businesses are no longer solely reliant on humans to manually process every natural language message, meaning more requests can be processed in less time.
- Lower operating costs
NLP can reveal new change opportunities and sources of inefficiency that were previously hidden.
- More scalable automation
By making natural language understandable to machines, NLP allows automation to be scaled into comms-based workflows once thought impossible to automate.
- Higher productivity
When leveraging NLP tools, employees can delegate repetitive comms work and focus more on value-add.
- Improved customer and employee retention
Faster service and less manual comms processing leads to more satisfied customers and employees.
Book a demo to see what Re:infer’s NLP-based platform can do for your organisation.
What can Natural Language Processing be used for?
There are many different use cases for the broad category of NLP. The ability to rapidly understand masses of natural language data is crucial across countless business contexts. The technology can create value in any industry where the processing of information is critical to the running of an enterprise.
Conversational Data Intelligence
Through collaboration between NLP and human employees, Conversational Data Intelligence creates structured data from masses of unstructured communications expressed in natural language. This gives users the ability to analyse previously hidden business processes and automate low-skill manual processes that used to depend on human reading comprehension.
Learn more about Conversational Data Intelligence
Conversation Intelligence uses NLP to record and understand the spoken words of humans, picking out the most important parts of the conversation for later analysis. It is most commonly used to analyse previous conversations with prospects and customers and to help users improve their techniques for future interactions.
Chat bots are solutions which simulate human-like interactions through text on digital channels. They use NLP to understand and respond to questions from human users. Chat bots are most commonly used in customer or business service functions to automate the answering of common user questions.
Speech recognition is a crucial component in virtual assistant and automated customer service solutions. NLP helps these systems understand the spoken words of clients, enabling them to trigger the correct response or best next action.
Most translation solutions leverage NLP to understand raw text and translate it into another language. Machine translation solutions are typically used to translate large amounts of natural language information in a short period of time.
Text summarisation is the process of summarising the key information contained in large texts for easier consumption. NLP is a vital part of the process as it enables the machine to understand the text and infer the most important information.
NLP is commonly used to identify and extract opinions from a large body of different texts, to discover common themes and ideas. Sentiment analysis is useful for quickly understanding the attitudes of a large group of people, such as a company’s workforce or an entire market.
Topic modelling is a popular topic analysis application, which leverages NLP to detect word and phrase patterns within a large body of texts. It then clusters these word groups and similar expressions to characterise a given set of documents.
What’s the history of Natural Language Processing?
NLP has been an important academic field for decades. It grew out of linguistic theory and the development of machine translation systems in the 1940s and 1950s. The first NLP and machine translation models were created in the 1960s and were heavily rules-based. It wasn’t until the 1980s that statistical models were developed that allowed algorithms to make probabilistic decisions and predictions about human language.
Since the early 2000s, a subfield of machine learning known as deep learning has driven the most significant NLP developments. Deep neural network architectures have become widespread in NLP due to their strong performance in tasks like language modeling and parsing.
However, NLP has only become truly reliable for real-world business applications within the last few years. This is down to several key developments:
In the early 2010s, the development of the word2vec algorithm transformed how NLP models understand human language. By mapping distinct words onto strings of numbers called vectors, word2vec allowed NLP to understand the relationship between words in a message - not just what they mean, but how they link together to create meaning.
Convolutional neural networks
Convolutional neural networks (CNN) have traditionally been used for computer vision and image recognition applications. However, their use has also been explored for NLP. CNNs are able to model different contextual realities of language, which has achieved great advances in applications like semantic parsing, search query retrieval, sentence modeling, classification and prediction.
Recurrent neural networks
Recurrent neural networks (RNN) have become a widely used architecture for NLP. Unlike traditional neural networks, an RNN takes individual words rather than entire samples as its default input. This allows NLP models the flexibility to work with varying sample lengths, and enables the sharing of features learned across different positions of text. An RNN creates a sort of internal memory for a model, enabling previous inputs to inform subsequent predictions. This has greatly improved the accuracy and consistency of predictions, especially at the level of word recognition.
Long short-term memory
Long short-term memory (LSTM) is an artificial RNN architecture that can process entire sequences of data in addition to single data points. LSTM remembers values over extended time intervals, giving machine learning models an internal memory that gets closer to human contextual understanding. This has led to very important improvements in NLP, enabling models to infer meaning and make predictions based on how certain words and phrases were used previously in an extended text.
GLUE and superGLUE benchmarks
Even when NLP models started to produce useful outcomes, their accuracy and performance struggled to match those of humans in reading comprehension tests. However, this picture has changed radically in the last few years.
The General Language Understanding Evaluation (GLUE) benchmark was established in 2018 to test and compare different NLP models while comparing them to a human baseline in terms of language understanding. However, within a year model performance came so close to the human benchmark that a new benchmark - SuperGLUE - was created, offering a broader range of more difficult and varied tests. This has since been surpassed, with new NLP models now routinely outmatching human performance in language understanding and reading comprehension.
What are the most important trends in Natural Language Processing?
The advent of Transformers
Transformers are giant language models trained from datasets of unprecedented size and complexity. This has radically improved the accuracy and reliability of NLP solutions, and has enabled them to deliver useful outcomes for a wide range of real-world applications.
Unprecedented access to data and compute
Closely related to the rise of Transformers has been the emergence of the data and technology needed to develop them. It is only within the last ten years that huge, publicly available datasets have been made available for NLP development and active use. Similarly, the computing power needed to create and run effective NLP models has only recently become available thanks to the latest generation of computing hardware.
Attention is turning to making NLP models that are faster to train and less resource-intensive. This is driven by environmental concerns but also the need to create NLP applications that can run on less powerful mobile hardware.
Awareness is growing that AI algorithms have a tendency to amplify existing biases in limited datasets and produce outcomes that can be seen as unfair. Important work is being done to develop more balanced NLP models that are more aware of statistical imbalances and which can act to rectify them.
NLP remains a highly complex and time-consuming field to participate in, but this shouldn’t prevent others from leveraging powerful NLP in their work and daily lives. A new generation of low code and no code NLP solutions has emerged which makes model training fast and easy, even for people with zero technical experience or qualifications.
What are the most important Natural Language Processing tools?
NLTK is an important platform for building Python programs to work with natural language data. It provides a suite of text processing libraries for processes including classification, tokenisation, stemming, tagging, parsing, and semantic reasoning.
The Stanford Natural Language Processing Group
Stanford’s NLP research group makes available some of its most powerful NLP solutions. It provides free tools for statistical NLP, deep learning NLP, and rule-based NLP which can be easily integrated into applications with natural language requirements.
TensorFlow is an end-to-end open-source platform for machine learning, using data flow graphs to build models for applications like NLP. It enables engineers to develop large-scale neural networks with many layers.
The Python programming language offers many tools and libraries for building NLP programs. Its Natural Language Toolkit is an open-source hub of resources, libraries and programs for NLP development.
PyTorch is a free, open source machine learning library that helps speed up the process from research prototyping to production deployment.
Hugging Face is a leading open-source provider of NLP technologies. It offers valuable libraries of NLP models, transformers and datasets which developers can use when building their own NLP systems.
How do I get started with Natural Language Processing?
Once you have decided what task or tasks you want to use NLP for, you should carefully consider whether you should build your own proprietary NLP system or purchase an existing solution from a vendor. This will depend on your needs, but it’s important to remember that few organisations will have the time, manpower or resources needed to build an effective NLP solution from scratch.
Learn more about the challenges of building proprietary NLP systems.
Many organisations will find the best option to be implementing an existing solution that has already been tested and proven by a technology vendor. This also holds the advantage of delegating the maintenance and improvement of the platform to an expert provider.
What is NLP for email?
As the accuracy and performance of NLP models increases, so too does the use of the technology in real-world business contexts. The use of NLP for email classification, routing, analysis and automation has grown steadily over the last few years. Businesses are seeking to reduce the manual effort expended by their staff to process and complete email-based tasks and requests - especially as email remains one of the most popular and important channels for customer communication.
Today's Transformer-based NLP models have the sophistication and 'long-term memory' required to understand and interpet emails - not just at the level of individual messages but entire email threads and conversations. With the latest models now routinely outperforming human agents, NLP is the ideal solution to automate the interpretation and analysis of email conversations within an enterprise.
Learn more about how NLP is transforming email communications in the enterprise.
The most popular NLP for email use cases include:
NLP email classification
Many workflow systems use NLP capabilities to automatically extract key information (such as sender domain and subject line elements) and categorise them. They are often filtered into request queues and buckets for the purposes of analysis or processing. This is usually done to reduce manual effort on the part of human agents.
Email to Case
NLP platforms like Re:infer, a UiPath company, understand the intent and content of inbound emails. This enables email-to-case capabilities, automatically migrating business processes from email channels to workflow applications. This helps to automate case creation, processing events, status requests, and monitors for new processes being performed outside of workflow.
Learn more about email to case
Some NLP solutions provide a fully automated triage capability where inbound requests are automatically routed to the right team or person, removing the process from agent workflows. Fine-grained control is usually given over rules for classification, prioritisation, data extraction, and routing.
Re:infer and NLP
The Re:infer Conversational Data Intelligence Platform provides full, no-code NLP capabilities that start delivering value after a short training period - making it an ideal solution for the enterprise. Re:infer has worked with many companies to extract the value from conversational data and build intelligent products, services and workflows.
Re:infer uses the latest innovations and machine learning techniques to overcome the challenges most commonly faced by NLP solutions.
How does Re:Infer create value from natural language quickly, without lengthy model training periods?
Re:infer uses a no-code approach to Active Learning, where users with zero technical experience can rapidly train and prepare powerful NLP models for deployment. Through a process of entity extraction, Re:infer automatically discovers the key parts of messages - with the role of the subject matter expert being simply to review and correct the model’s predictions. This process trains models quickly so they can start generating value for the business much faster.
How does Re:infer process and understand different languages?
Re:infer models are trained from masses of multilingual data. Through a process of multilingual intent recognition, Re:infer learns to understand the common themes and concepts behind words in different languages. Re:infer models will recognise the common meanings behind different words, regardless of what language they are expressed in. This even allows the platform to understand messages which contain numerous different languages.
Can Re:infer understand vernacular language and industry-specific jargon?
With training, Re:infer can be taught to understand any specialist language, no matter how customised. Re:infer models come pre-trained on industry-specific generic language depending on the needs of the client. The role of subject matter experts is then to fine-tune the models so they can understand and recognise the exact nomenclature required.
Can Re:Infer make sense of variations and incorrect spelling?
Re:infer’s models go far beyond simple keyword analysis. Re:infer isn’t just trained on proper language, as used in formal documents and communications, but on real-world informal language as well - including any common spelling mistakes and variations. Re:infer will recognise the intent and meaning behind the word, no matter how it is spelt.