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Natural Language Processing: Uses, Benefits and everything else

Natural language processing (NLP) is a branch of computer science and artificial intelligence (AI) that studies how computers and humans interact in natural language. NLP has become increasingly important as AI language models such as ChatGPT revolutionize the way we approach NLP.

What is natural language processing (NLP)?

Natural language processing (NLP) is when a computer figures out what a person is saying.

All languages have rules about grammar and meaning, but there are always exceptions. The same word in the same language can mean something completely different in different places and situations, even if it is the same word. So, if it’s hard for someone who speaks English, for example, to understand Spanish, it will also be hard for a machine. Machine learning is a tool that can be used to help solve this particular problem.

Machine learning is a process that takes data and turns it into knowledge. The performance of NLP can be improved by using machine learning.


Both, natural language processing (NLP) and machine learning are subsets of artificial intelligence. Both share techniques, algorithms, and knowledge. NLP has been around for over 50 years and has its roots in linguistics. It has a wide range of real-world applications, including medical research, search engines, and business intelligence.

How natural language processing works?

NLP enables computers to understand natural language in the same way that humans do. Natural language processing, whether spoken or written, employs artificial intelligence to take real-world input, process it, and make sense of it in a way that a computer can understand. Computers, like humans, have different sensors, such as ears to hear and eyes to see, and microphones to collect audio. And, just as humans have a brain to process that input, computers have a program to do the same. At some point during the processing, the input is converted to computer-readable code.

Natural language processing is divided into two stages: data preprocessing and algorithm development.

Data preprocessing entails preparing and “cleaning” text data so that machines can analyze it. Preprocessing converts data into usable form and highlights textual features that an algorithm can use. There are several ways to accomplish this, including:

  • Tokenization – Text is broken down into smaller units to make it easier to work with.
  • Stop word removal – This is when common words are removed from text, leaving only the unique words that provide the most information about the text.
  • Stemming and lemmatization – To process, words are reduced to their root forms.
  • Speech tagging – This is when words are labeled according to their part of speech, such as nouns, verbs, and adjectives.

An algorithm is created to process the data after it has been preprocessed. There are many different types of natural language processing algorithms, but two of the most common are:

  • System based on rules. This system employs carefully crafted linguistic rules. This method was used early in the development of natural language processing and is still used today.
  • A system based on machine learning. Statistical methods are used by machine learning algorithms. They learn to perform tasks by being fed training data, and they adjust their methods as more data is processed. Natural language processing algorithms refine their own rules through repeated processing and learning using a combination of machine learning, deep learning, and neural networks.

natural language processing

What are the uses of natural language processing?

Natural language processing algorithms perform the following primary functions:

  • Machine translation. This is the process by which a computer automatically translates text from one language, such as English, to another, such as French.
  • Text categorization. This entails categorizing texts by assigning tags to them. This is useful for sentiment analysis, which assists a natural language processing algorithm in determining the sentiment, or emotion, behind a text. When brand A is mentioned in X number of texts, for example, the algorithm can determine how many of those mentions were positive and how many were negative. It can also be used for intent detection, which predicts what the speaker or writer will do based on the text they produce.
  • Extraction of text. This entails automatically summarizing text and locating relevant data. One example is keyword extraction, which extracts the most important words from text for use in search engine optimization. Using natural language processing for this requires some programming; it is not entirely automated. However, there are numerous simple keyword extraction tools available that automate the majority of the process; the user only needs to set parameters within the program. A tool, for example, could extract the most frequently used words in the text. Another example is named entity recognition, which uses text to extract the names of people, places, and other entities.
  • Natural language generation. This entails using natural language processing algorithms to analyze unstructured data and generate content automatically based on that data. One example is language models like GPT3, which can analyze unstructured text and then generate believable articles based on it.

Natural language processing research is centered on search, particularly enterprise search. This entails asking users to query data sets in the form of a question they might ask another person. The machine interprets the key elements of a human language sentence that correspond to specific features in a data set and returns an answer.

NLP can be used to interpret and analyze free, unstructured text. Free text files, such as patient medical records, contain a massive amount of information. Prior to the advent of deep learning-based NLP models, this data was inaccessible to computer-assisted analysis and could not be analyzed in any systematic manner. Analysts can use NLP to sift through massive amounts of free text to find relevant information.

Sentiment analysis is another important application of NLP. Data scientists can use sentiment analysis to assess social media comments to see how their company’s brand is performing, or to review notes from customer service teams to identify areas where customers want the company to perform better.

Natural language processing methods and techniques

The two main techniques used in natural language processing are syntax and semantic analysis.

The arrangement of words in a sentence to make grammatical sense is known as syntax. NLP employs syntax to evaluate the meaning of a language based on grammatical rules. Among the syntax techniques are:

  • Parsing. This is a sentence grammatical analysis. For instance, suppose a natural language processing algorithm is fed the sentence “The dog barked.” Parsing is the process of breaking this sentence down into parts of speech, such as dog = noun, barked = verb. This is advantageous for more complicated downstream processing tasks.
  • Segmentation of words. This is the process of deriving word forms from a string of text. A person, for example, scans a handwritten document into a computer. The algorithm could analyze the page and determine that the words are separated by white spaces.
  • Sentence fragmentation. In large texts, this creates sentence boundaries. As an example, the text is fed into a natural language processing algorithm “The dog yipped. I came to.” The algorithm can recognize the period that is used to break up sentences.
  • Segmentation based on morphology. This breaks down words into smaller parts known as morphemes. The word untestably, for example, would be broken down into [[un[[test]able]]ly], where the algorithm recognizes “un,” “test,” “able,” and “ly” as morphemes. This is particularly useful in machine translation and speech recognition applications.
  • Stemming. This separates words with inflection into root forms. For instance, in the sentence “The dog barked,” the algorithm would recognize that the root of the word “barked” is “bark.” This would be useful if a user was looking for all instances of the word bark, as well as all of its conjugations, in a text. Even though the letters are different, the algorithm recognizes that they are essentially the same word.

Semantics is concerned with the use and meaning of words. Algorithms are used in natural language processing to understand the meaning and structure of sentences. Among the semantic techniques are:

  • Disambiguation of words. This method derives the meaning of a word from its context. Consider the following sentence: “The pig is in the pen.” The word pen has different meanings. This method allows an algorithm to understand that the term “pen” refers to a fenced-in area, not a writing implement.
  • Recognized named entities. This determines which words can be classified into groups. For example, using this method, an algorithm could analyze a news article and identify all mentions of a specific company or product. It would be able to distinguish between visually similar entities using the semantics of the text. In the sentence “Daniel McDonald’s son went to McDonald’s and ordered a Happy Meal,” for example, the algorithm could recognize the two instances of “McDonald’s” as two separate entities — one a restaurant and one a person.
  • Natural Language creation. A database is used to determine the semantics of words and generate new text. For instance, an algorithm could automatically generate a summary of findings from a business intelligence platform by mapping specific words and phrases to data features in the BI platform. Another example would be generating news articles or tweets automatically based on a body of text used for training.

Deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding, is at the heart of current approaches to natural language processing. Deep learning models necessitate massive amounts of labeled data for the natural language processing algorithm to train on and identify relevant correlations, and assembling such a large data set is one of the most significant challenges in natural language processing.

Previously, simpler machine learning algorithms were told what words and phrases to look for in text and were given specific responses when those phrases appeared. Deep learning, on the other hand, is a more adaptable, intuitive approach in which algorithms learn to identify speakers’ intent from a large number of examples, similar to how a child learns human language.

Natural Language Toolkit (NLTK), Gensim, and Intel Natural Language Processing Architect are three popular natural language processing tools. NLTK is a Python open source module that includes data sets and tutorials. Gensim is a Python topic modeling and document indexing library. Another Python library for deep learning topologies and techniques is Intel NLP Architect.

What are the benefits of natural language processing?

The primary advantage of NLP is that it improves how humans and computers communicate with one another. Code — the computer’s language — is the most direct way to manipulate a computer. Interacting with computers becomes much more intuitive for humans as computers learn to understand human language.

Other advantages include:

  • Less expensive: It costs less to use a program than to hire a person. It can take a person two or three times as long as a machine to do the above tasks.
  • Faster response times for customer service: When NLP is used, chatbot or phone response times are usually very fast. Most call centers have a small number of employees, which limits the number of calls that can be taken. By using NLP, more calls can be answered, which means clients have to wait less.
  • Easy to use: In the past, people who wanted to use NLP had to do a lot of hard research on the language and do a lot of tasks by hand. When it came to translation, there were many times when it was necessary to make a kind of dictionary with words that could be literally translated into another language. So, it took a long time to come together. But now, it’s easy to find machine learning models that have already been trained and can help with different NLP applications.

Natural language processing history

NLP is based on developments in computer science and computational linguistics dating back to the mid-twentieth century. Its evolution included the following significant turning points:

  • 1950s. Natural language processing can be traced back to this decade, when Alan Turing devised the Turing Test to determine whether or not a computer is truly intelligent. As an intelligence criterion, the test involves automated interpretation and natural language generation.

1950s-1990s. NLP was largely rule-based, with linguists developing handcrafted rules to determine how computers would process language.

  • 1990s. Because advances in computing made this a more efficient way of developing NLP technology, the top-down, language-first approach to natural language processing was replaced with a more statistical approach. Computers were becoming faster, allowing them to be used to develop rules based on linguistic statistics without the need for a linguist to create all of the rules. During this decade, data-driven natural language processing became popular. Instead of delving into linguistics, natural language processing shifted from a linguist-based approach to an engineer-based approach, drawing on a broader range of scientific disciplines.
  • 2000-2020s. The term “natural language processing” has grown in popularity dramatically. Natural language processing has gained numerous real-world applications as computing power has increased. Approaches to NLP today combine classical linguistics and statistical methods.
    Natural language processing is critical to technology and how humans interact with it. It is used in a wide range of real-world applications, including chatbots, cybersecurity, search engines, and big data analytics. Despite its challenges, NLP is expected to remain an important part of both industry and daily life.

Real life examples of NLP

Email filters
NLP started online with email filters. Spam filters caught phrases. Early NLP enhanced filtering. New NLP classifies Gmail emails. Content determines primary, social, or promotional emails. Gmail users can sort essential emails.

Smart Assistants
“Hello Siri” and contextual responses are familiar. Siri and Alexa are on thermostats, light switches, cars, and more. Alexa and Siri should comprehend contextual clues to simplify our lives and order products, and we like when they respond playfully or answer questions about themselves. We’ll meet helpers. “Something greater is afoot,” said the New York Times. Alexa will likely be the decade’s third big consumer computing platform.

Results NLP helps search engines surface relevant results based on comparable search habits or user intent so average users may find what they need without a search-term wizard. Google displays flight status, stock information, or a calculator when someone types a flight number or math equation. Search NLP provides relevant results for ambiguous searches.

Smartphone users demand predictive text, autocorrect, and autocomplete. Autocorrect clarifies words. Teach. Idiom-learning predictive text. Fun sharing predictive text sentences. Media reported the surprise personal and enlightening results.

Spanish homework cheating. Translators overlooked sentence structures in various languages. NLP aids online translators. Foreign language communication benefits. Tools translate foreign content into your language.

VoIP calls
“This call may be recorded for training” is rarely questioned. If a customer is upset, these recordings may be utilized for training, but they usually go into the NLP database to learn and grow. Chatbots or service reps answer customer calls. NLP enables computer speech. This video shows Google Assistant scheduling your appointment with dentist.

Many BI tools integrate natural language into data visualization. Data semantics aid smarter visual encodings. . Language improves data analytics for all firms, not just analysts and software engineers. This webinar demonstrates how natural language enhances data visualization and exploration.

Unstructured text analysis uses statistical, linguistic, and machine learning methods. NLP tools can analyze customer feedback and brand mentions. These encounters may help firms examine marketing efforts or identify consumer issues before enhancing service. When businesses realize its usefulness, NLP has many digital applications.



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