Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP stems from several disciplines, including computer science and computational linguistics, that seek to bridge the gap between human communication and understanding computers.
Evolution of natural language processing
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While natural language processing is not a new science, this technology is advancing rapidly thanks to the growing interest in human-machine communication, alongside the availability of big data , more powerful computing and improved algorithms.
As a human, you can speak and write in English, Spanish or Chinese. But a computer’s native language – known as machine code or machine language – is highly incomprehensible to most people. At the deepest levels of your devices, communication happens not with words, but through millions of 0s and 1s that produce logical actions.
70 years ago, programmers used punch cards to communicate with the first computers. This laborious, manual process was understood by a relatively small number of people. Today you can say “Alexa, I like this song”, and a device in your home will turn down the volume and respond “Ok, rating saved” in a voice that simulates a human being. It then adapts its algorithm to play that song – and others like it – the next time you listen to that station.
Let’s look at this interaction in detail. His device was activated when he heard you speak, understood the intention between the lines of the comment, performed an action and gave feedback in a well-constructed sentence, all in about five seconds. Full interaction is only possible thanks to NLP in conjunction with other artificial intelligence technologies such as machine learning and deep learning.
What is the importance of the NLP?
Large volumes of textual data
Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, PLN makes it possible for computers to read texts, listen and interpret speeches, identify feelings and determine which passages are important.
Today’s machines can analyze more language-based data than humans can, without fatigue, consistently and impartially. Considering the massive amount of unstructured data that is generated every day, from medical records to social media, automation will be imperative for complete and efficient text and speech analysis.
Structuring a highly unstructured data source
Human language is surprisingly complex and diverse. We express ourselves in endless ways, both verbally and in writing. Not only are there hundreds of languages and dialects, there is also a unique set of grammatical and syntax rules, expressions and slang within each one. When we write, we often make mistakes or abbreviate words, or omit punctuation; when we speak, we carry regional accents, tend to murmur and borrow terms from other languages.
Although supervised learning, unsupervised learning, and specifically deep learning are now widely used to model human language, there is also a need for syntactic and semantic understanding, as well as mastery, which are not necessarily present in these machine approaches. learning. PLN is important because it helps to resolve ambiguity in the language and adds useful numerical structure to the data for many downstream applications such as speech recognition or text analysis.
How does the NLP work?
Separating the elemental parts of language.
Natural language processing incorporates diverse techniques for interpreting human language, from statistical and machine learning methods to algorithmic and rule-based approaches. We need a good variety of approaches, because text-based or voice-based data differ widely, as do their practical applications.
Basic NLP tasks include tokenization and parsing, lemmatization/stemization, labeling of speech components, language detection and identification of semantic relationships. If you ever structured prayers in school, then you’ve done all these tasks by hand.
Broadly speaking, NLP tasks break language down into smaller, essential pieces, try to understand the relationships between them, and explore how those pieces work together to create meaning.
These underlying tasks are often used in more complex levels of NLP, such as:
- Content categorization. A linguistic-based document summary, which includes search and indexing, content alerts, and duplicate detection;
- Topic discovery and modeling. Accurately captures meaning and themes in collections of text, and applies advanced analytics such as optimization and forecasting;
- Contextual extraction. Automatically extracts structured information from textual sources;
- Sentiment analysis. Identifies mood or subjective opinions in large amounts of text, including average sentiment and opinion mining;
- Speech-to-text and text-to-speech conversion. Turns voice commands into written text and vice versa;
- Summarization. Generate synopses of large bodies of text automatically;
- Machine translation. Translates text or speech from one language to another automatically.
In all these cases, the intended goal is to take the raw input and use linguistics and algorithms to transform or enrich the text in order to get better results.
PLN methods and applications
How computers understand textual data
NLP and text analysis
Natural language processing goes hand in hand with text analysis, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and find new raw text variables, which can be viewed, filtered, or used as inputs to predictive models or other statistical methods.
NLP and text analytics are used together for many applications, including:
- Investigative Discovery. Identifies patterns and clues in emails or written reports to help detect and solve crimes;
- Specialized knowledge. Categorizes content into meaningful topics so you can take action and discover trends;
- Social media analysis. Track relevance and sentiment on specific topics, and identify influencers.
Examples of NLP in everyday life
There are many practical and common applications for NLP in our everyday lives. In addition to talking to virtual assistants like Alexa or Siri, here are some other examples:
- Have you ever looked at the emails in your spam box and noticed similarities in subject lines? You’re looking at a Bayesian spam filter, a NLP statistical technique that compares the most common words in spam messages to validate emails and identify junk mail;
- Have you ever missed a call and had your automatic transcript read over email or in an app? This is speech-to-text conversion, an NLP capability;
- Have you ever navigated a website using its built-in search tool or by selecting a suggested topic, entity or tags? So you’ve already used NLP methods for searching, topic modeling, entity extraction, and content categorization.
A subfield of NLP called Natural Language Understanding (NLP) has started to gain popularity thanks to its potential in cognitive and AI applications. ELN pushes the boundaries of structural understanding of language to interpret intent, resolve word and contextual ambiguities, and even create well-formed human languages on its own. ELN algorithms must solve the extremely complex problem of semantic interpretation – that is, understanding the intended meaning of spoken or written language, with all its subtleties, contexts and inferences that we humans are capable of understanding.
The evolution of the PLN to the ELN has very important implications for companies and consumers. Imagine the power of an algorithm that can understand the meaning and nuance of human language in contexts ranging from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers’ relentless ability to understand everything.
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