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Date2022-03-10 10:00
Read10min

Our Guide to Natural Language Processing, an Introduction to NLP

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.

Our Guide to Natural Language Processing, an Introduction to NLP

What is Natural Language?

In this context, natural language refers to language produced by humans. It includes everything from books and recorded speech to the language content in invoices and emails. Processing natural language presents significant challenges for computer systems.

With the discovery and growth of computer science, natural language has always been a topic of automation in research. But the language used by humans is highly complex and has many features that make it very difficult for a computer to use. The many ambiguities of human language are not readily compatible with a computer's "understanding". Variations in sentence structure, meaning changes based on context, homonyms, metaphors -- these and many other features make it hard for computer software to interpret language accurately. 

That's where natural language processing (NLP) comes in, helping to break down language into elements that computer software can interpret more effectively.

What is Natural Language Processing (NLP)?

In the context of artificial intelligence, NLP refers to the broad field of language manipulation by software. The main focus of NLP is to enable computers to understand human language, both text and speech, to interpret it, and in some cases to produce language that's meaningful to humans.


Computational linguistics

Computational linguistics is the science of modeling human language based on a system of rules. It's an interdisciplinary field that covers language modeling and computational approaches to problems relating to linguistics.

Natural language processing applies a combination of computational linguistics and other techniques. Deep learning models, statistical models and machine learning models are all relevant to NLP.


What Natural Language Processing is not

Natural language processing should not be confused with neuro-linguistic programming. Although they're both abbreviated to NLP, the two concepts are unrelated. Neuro-linguistic programming is an approach to interpersonal communication and psychotherapy, with NLP practitioners working in the field of self-improvement.

What is the History of Natural Language Processing (NLP)?

NLP as a science has been around since the birth of modern computing. When the first true computers were being developed, it became necessary to find ways to give them instructions. As computers became more complex, these instructions needed to be more complex as well. The field of computational linguistics began in response to this need. The problems associated with processing natural language have not really been solved, even after 50 years of study. It's an ongoing search that has produced a great deal of useful knowledge and technological development.


How Does Natural Language Processing (NLP) work?

Given the huge variety of data, NLP has to encompass a very wide range of different techniques and approaches to the problem of interpreting natural human language. Text and speech create different challenges, and the approach used will also be affected by the intended application.


Natural Language Processing tasks

To generalize, NLP tasks consist of breaking down language into smaller elements and determining the relationships between them. At the basic level, tasks include language detection, tokenisation, parsing, tagging parts of speech, and the identification of relationships between words. While these tasks are simple enough to undertake manually, they would be extremely time-consuming. Automating the tasks using sophisticated computer algorithms is imperative. Examples of tasks include:

Speech recognition: The conversion of audio voice data into text. Speech recognition is also known as speech-to-text. It's an especially challenging task. Even within a single language, there may be many different regional variations in accent and dialect. Furthermore, different speakers will speak in different ways; they may slur their words, stutter, or use non-standard grammar. Reliably turning speech into text is notoriously difficult to accomplish, yet the technology is progressing at an amazing rate.

Grammatical tagging: Sometimes called part of speech tagging, this process involves determining what part of speech a word represents -- whether a word is a noun, a verb, etc.

Named entity recognition: NER, as it's known, is the process of identifying words or phrases that refer to a specific entity. For example, NER could identify "Australia" as a physical location, "John Smith" as a person's name, or "Pump number 12" as a piece of machinery in a factory setting. A related concept is co-reference resolution, the identification of multiple terms that refer to the same entity.

Word sense disambiguation: Many words have two or more different meanings based on context. For example, the word "mind" could mean to be careful of something or it could refer to the human intellect. Disambiguation is necessary to interpret ambiguous words correctly.

Natural language generation: This can be seen as the inverse of speech recognition. In natural language generation, an AI is tasked with producing appropriately structured text or speech in human language. A closely related concept is statistical NLP (SNLP) uses statistical methods to predict the next character, word, or phrase in a given sequence based on the previous content.

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Natural Language Processing (NLP) and Data Labeling

Data labeling is crucial for NLP to be effective. If data elements aren't tagged appropriately and meaningfully, it will not be possible for the AI behind the process to accurately understand the input. 


There are numerous different tools available which can automate data labeling and ensure that it's done efficiently and effectively. The specific annotation tool you'll need for your data will depend on your specific goals and applications. It's important to consider the type of data that you'll be working with, the levels of security required, and the final goals of the project when selecting a tool.

Examples of Natural Language Processing (NLP) Use Cases

Examples of natural language processing in use are many and varied. One very important application of NLP is in knowledge management -- processing and categorizing information. Natural language processing can help computers to interpret various types of content, from documents to audio recordings, and categorize it for storage, retrieval, data mining, etc.

Here are some further NLP use cases:


Machine translation: Computer translation between languages is becoming more and more widely used. Examples include BabelFish, Google Translate and DeepL. These systems allow for users to input text (and in some cases speech or images with characters) in one language and output it to another. While machine translations are not as accurate as translations made by a skilled human translator, machine translations are still tremendously useful and are becoming more accurate over time.

Spam detection: Early spam filters relied on relatively clumsy methods -- blocking addresses from endlessly growing lists of known bad actors, detecting large blocks of duplicate text, looking for specific phrases, etc. Today's spam filters use natural language to understand an email's content and determine whether or not it's a genuine email or spam. Systems are now sophisticated enough to detect threatening language, multiple misspellings, discussion of financial transactions, and other indicators. Instead of blocking based on a handful of basic rules, modern spam filters can detect patterns within the text that denote phishing or irrelevant commercial mass-mailings.

Text summarisation: Using NLP, it is now possible to break down vast amounts of digital text, interpret them and generate summaries. These can be used to provide synopses of the content, produce indexes and databases for research, or simply to reduce the workload of readers who don't need to peruse the entire text. Text summarisation may also make use of NGL to produce meaningful and context-rich summaries.

Sentiment analysis: By analyzing posts on social media, reviews and similar material, NLP can uncover a great deal of hidden data relating to public sentiment on a particular topic. Services, products, events and product designs can all generate a lot of online discussion, which can then be analyzed to discover how the public feel about it, revealing opinions, preferences and beliefs. This is a hugely valuable tool for any business.

Chatbots: NLP is used to make advanced chat-bots that can better understand input from human operators. NLP makes these chat-bots more responsive and natural, enabling them to handle customer queries, provide education on a range of topics or simply provide entertainment. Natural language processing is also used for virtual assistants such as Siri and Alexa, allowing them to understand requests and follow instructions spoken by the user.

Natural Language Processing (NLP) and Knowledge Management

Knowledge management (KM) refers to the process of making the best use of an organization's collected knowledge. Today's businesses and organizations generate huge volumes of information, largely in the relatively concrete form of documents and digital files but also in the form of knowledge held by the people who make up the organization. Managing all of this information effectively is a colossal task, and one which only gets larger as a business grows and develops over time. Knowledge management is necessary to ensure that relevant information is stored and organized in such a way that it can be handled and disseminated efficiently.

Proper knowledge management systems are valuable to institutions because they ensure that all employees and leaders within the company have ready access to information that they need, when they need it. KM can help identify errors and avoid them in the future, develop strategies, locate areas where efficiency can be improved, and more. They can also help integrate useful data from outside of the organization, such as social media discussion of a new product or reviews of a service.

KM helps to preserve knowledge. Individuals, especially in the higher levels of the company, are often walking repositories of useful information. If, say, John Smith has knowledge that's relevant to multiple departments within an organization, it's not efficient for him to waste time fielding multiple queries throughout the day. If John Smith gets promoted, transferred or leaves the company, his valuable knowledge might end up going with him. With good knowledge management, his know-how can be preserved and made accessible to others.

NLP is a valuable tool for knowledge management. It can allow computer systems to identify and interpret different pieces of data, categorise them and store them appropriately. For instance, software using NLP could identify a particular document as an invoice, a report, an inquiry from a prospective client, or a request for supplies. The item can then be appropriately tagged, categorised and stored ready for retrieval.

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