Definition of NLP Natural Language Processing
NLP (full name Natural Language Processing), namely natural language processing, is a field of computer science focused on creating computers and software that can understand human speech and language.
The languages of humans and computers 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, while computers are entirely based on logic, following the instructions they are programmed to execute. This difference means that traditionally computers have difficulty understanding human language, and 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, figure out intent and sentiment, and form responses. As we will see, the applications of natural language processing are very broad and numerous.
The Importance of NLP Natural Language Processing
The concept of natural language processing predates what you might think; as early as the 1950s, experts had been looking for ways to program computers for language processing. In recent years, with improvements in computing power andmachine learningdevelopment, the field has made tremendous progress.
When we think about the importance of NLP, it is worth considering how human language is structured. In addition to the vocabulary, syntax, and grammar that make up written sentences, there is also the phonetics, tone, accent, and phrasing of spoken language.
We convey meaning in many different ways, and the same word or phrase can have completely different meanings depending on the background and intent of the speaker or writer. Essentially, language is sometimes even difficult 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 produce better human-computer interaction, as well as provide detailed insights about intent and sentiment, allowing businesses, ordinary consumers, and technical personnel to benefit from it.
How NLP Natural Language Processing Works
Now that we have some understanding of what natural language processing is, let us take a deeper look at the basics of how it works. It is worth noting that NLP is a fairly advanced application of artificial intelligence, so we will look at a very top-level explanation of the software.
The first thing about natural language processing is that several functions or tasks make up this field. Depending on the required solution, some or all of these functions may interact at the same time. At a basic level, NLP software performs three main processes:
- First, it breaks down language (speech or text) into small pieces
- Second, it tries to understand the relationships between these pieces of information
- Finally, it tries to create meaning from these relationships
Of course, there are many more steps involved in these processes. A great deal of linguistic knowledge is required, as well as programming, algorithms, and statistics.
Technologies Involved in Natural Language Processing
As 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 speech down into individual sounds and uses algorithms to piece together meaning.
- Word sense disambiguation. In many languages, the same word can have different meanings depending on the context. Word sense disambiguation (WSD) is the process of analyzing language to distinguish between different meanings.
- Coreference resolution. This process helps determine whether 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.
- Sentiment 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), and it also uses elements of machine learning and data analysis. Although these are different fields, they overlap.
Each field is driven by large amounts of data, and the more data there is, the better the results. Bringing structure to highly unstructured data is another characteristic. Likewise, each field can be used to provide insights, highlight patterns, and identify current and future trends.
Natural language processing has many of these attributes because it is built on the same principles. Artificial intelligence is a field focused on machines simulating human intelligence, while natural language processing focuses specifically on understanding human language. Both are built on machine learning—using algorithms to teach machines how to complete tasks automatically and learn from experience.
Applications and examples of NLP natural language processing
- 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 makes no sense, and many language translators must determine the input language as well as the output language. Earlier forms of language translation relied on so-called rule-based machine translation, which required a large number of grammar rules and dictionaries for both languages. More recent AI translation methods rely on statistical machine translation, which uses data from existing translations to inform future translations.
- Voice assistants. Whether it is Xiao Ai Tong Xue, Tmall Genie, or Xiaodu in China, or Siri, Alexa, or Google Assistant abroad, many of us use these NLP-powered voice assistants. These intelligent assistants use NLP to match users' speech or text input with commands and provide responses based on the request. Typically, they do this by recording and examining the frequency and sound waves of your voice and breaking it down into a small amount of code. An algorithm is then used to analyze this code to determine its meaning.
- Search engine results. Search engines have been part of our lives for a relatively 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 digital marketing, especially in the field of search engine optimization.
- Predictive text. You may have seen this application of natural language processing in several places. Whether it's on your smartphone keyboard, in a search engine search bar, or when you're writing an email, predictive text is quite prominent. This type of NLP studies how individuals and groups use language and makes predictions about what word or phrase will appear next. Machine learning models study the probability of which word will appear next and, based on that, make suggestions.