NLP (Natural Language Processing), or natural language processing, is a field of computer science that focuses on creating computers and software that can understand human speech and language.
Human and computer languages are very different, and programming languages exist as an intermediary between the two. The way we speak and write is very nuanced and often ambiguous, whereas computers are entirely logic-based, following the instructions they are programmed to carry out. This difference means that computers have traditionally had difficulty understanding human language, while natural language processing aims to improve the way computers understand human text and speech.
NLP uses artificial intelligence and machine learning, as well as computational linguistics, to process text and speech data, derive meaning from it, clarify intent and emotion, and form responses. As we will see, the applications of natural language processing are vast and numerous.
The importance of NLP natural language processing
The concept of natural language processing is older than you may realize. As early as the 1950s, experts have been looking for ways to program computers for language processing. In recent years, with the improvement of computing power and the development of machine learning , this field has made great progress.
When we think about the importance of NLP, it’s worth considering how human language is structured. In addition to the vocabulary, syntax, and grammar that make up written sentences, there is also the pronunciation, intonation, accent, and diction of spoken language.
We convey meaning in many different ways, and the same word or phrase can have completely different meanings, depending on the context and intent of the speaker or author. By its very nature, language is sometimes difficult even for humans to interpret, so getting machines to understand us is quite an achievement.
However, as natural language processing improves, we can better interface with the technology around us. It helps bring structure to what is essentially unstructured, which can make software smarter and even enable us to communicate better with other people.
Ultimately, NLP can help generate better human-computer interactions, as well as provide detailed insights into intent and emotion, benefiting businesses, ordinary consumers, and technologists alike.
How NLP natural language processing works
Now that we have some understanding of what natural language processing is, let’s dig into the basics of how it works. It’s worth noting that NLP is a fairly advanced application of artificial intelligence, so we’ll look at a very top-level explanation of the software.
The first thing to know about natural language processing is that there are several functions or tasks that make up this field. Depending on the desired solution, some or all of these functions may interact simultaneously. At a basic level, NLP software performs three main processes:
- First, it breaks down language (speech or text) into small chunks
- Second, it attempts to understand the relationships between these pieces of information
- Finally, it attempts to create meaning from these relationships
Of course, there are many more steps involved in these processes. Requires extensive knowledge of linguistics, as well as programming, algorithms and statistics.
Technologies involved in natural language processing
Listed below, we provide some further details on how natural language processing works on certain tasks:
- Speech Recognition. The process of converting speech data into text data, where speech recognition software breaks down speech into individual sounds and uses algorithms to piece together meaning.
- Word meaning ambiguity. In many languages, the same word can have different meanings depending on context. Word Sense Discrimination (WSD) is the process of analyzing language to distinguish different meanings.
- Core reasoning solved. This process helps determine if and when words in text or speech refer to the same entity, a good example is resolving which pronouns (he, she, they) refer to which person or object.
- emotion analysis. Language often has many subtleties, and NLP can be used to extract emotions and attitudes from language, whether positive or negative.
The relationship between NLP, AI and machine learning
Natural language processing is a branch of artificial intelligence (AI) that also uses elements of machine learning and data analysis. Although these are different fields, they overlap.
Every field is driven by massive amounts of data, and the more data, the better the results. Bringing structure to highly unstructured data is another feature. Likewise, each area can be used to provide insights, highlight patterns, and identify current and future trends.
Natural language processing shares many of these properties because it is built on the same principles. Artificial intelligence is a field focused on machines simulating human intelligence, while natural language processing is specifically focused on understanding human language. Both are built on machine learning—the use of algorithms to teach machines how to automate tasks and learn from experience.
NLP natural language processing applications and examples
- Language translation. One of the challenges of NLP is producing accurate translations from one language to another. This is a fairly mature field of machine learning and one that has made significant progress in recent years. Of course, there are many factors to consider here. Direct word-for-word translation often does not make sense, and many language translators must determine the input language as well as determine the output language. Older forms of language translation relied on so-called rule-based machine translation, which required extensive grammar rules and dictionaries in both languages. Recent AI translation methods rely on statistical machine translation, which uses data from existing translations to inform future translations.
- Voice assistant. Whether it is Xiaoai, Tmall Elf or Xiaodu in China, or Siri, Alexa, or Google Assistant abroad, many of us are using these NLP-driven voice assistants. These smart assistants use NLP to match the user’s voice or text input to commands, providing responses based on the request. Typically, they do this by recording and examining the frequencies and sound waves of your voice, and breaking them down into small bits of code. This code is then analyzed using an algorithm to determine its meaning.
- Search engine results. Search engines have been part of our lives for a long time. Traditionally, however, they have not been particularly useful for determining the context of what and how people search. Among them, semantic search is an area of natural language processing that can better understand the intent behind people’s searches (whether by voice or text) and return more meaningful results based on it. In addition to providing better and more intuitive search results, semantic search also has an impact on the field of digital marketing, especially SEO.
- Predictive text. You may have seen this application of natural language processing in a few places. Whether it’s on your smartphone keyboard, a search engine search bar, or when you’re writing an email, predictive text is pretty prominent. This type of NLP studies how individuals and groups use language and makes predictions about what words or phrases will come next. The machine learning model looks at the probability of which word will appear next and makes recommendations based on that.
