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Enhancing Natural Language Processing in the Digital Age

Published on
October 5, 2023
Natural language processing (NLP) is slowly creeping into the mainstream. Discover the uses of NLP as an AI technology and how it works in this post.

Artificial intelligence (AI) tools have entered mainstream tech conversations. AI tools like image and text generators are now commonly used for entertainment and business purposes.

Tools like ChatGPT have gained popularity due to their usefulness. They have also piqued public interest due to the intelligence and efficiency these AI models exhibit.

AI text generators like ChatGPT are made possible due to natural language processing (NLP) technology. While it isn't as popular a buzzword as artificial intelligence, NLP is a growing field of computer science research gaining significant attention and investments.

Experts predict that the NLP market revenue will reach $43 billion by 2025, nearly 14 times its value in 2017.

This article will delve into the technology behind natural language processing and how this branch of AI can impact several existing industries.

Key Takeaways

  • Natural language processing (NLP) pertains to the field of artificial intelligence dealing with machines that can understand and manipulate human language.
  • NLP has applications across various industries, including social media sentiment analysis, spam detection, and many more.
  • Python is the programming language of choice when developing NLP models.

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What is natural language processing?

Natural language processing (NLP) refers to the area of AI pertaining to building machines that can manipulate and understand human language. NLP models have capabilities that allow them to glean patterns from a text and generate textual pieces similar to those written by humans.

This discipline stems from computational linguistics, which uses computer science to understand how human language works. 

However, while computational linguistics leans more toward the theoretical, NLP is more practical. Computational linguistics comes up with theoretical frameworks, while NLP aims to produce models that can perform useful tasks.

NLP has two main subfields or categories: natural language understanding and natural language generation. NLP models can choose one of these subfields or combine both functions.

Natural language understanding

Natural language understanding (NLU) is an NLP subfield or subset that uses syntactic and semantic analysis of speech and text to identify a sentence's meaning. As the name suggests, its primary goal is to understand and determine the meaning of a given text.

In this context, syntax refers to a sentence's grammatical structure. Semantics refers to the meaning. To understand a sentence's meaning, NLU models establish a data structure that identifies the relationships between words and phrases.

An NLU approach to natural language processing is beneficial in sentiment analysis. For example, businesses could use NLU to understand consumer attitudes on social media based on their posts and activity.

Natural language generation

Natural language generation (NLG) is another subfield of NLP that aims to generate written content. While NLU focuses on reading comprehension, NLG focuses on getting computers or machines to write.

NLG models produce written text responses based on data input. These models aim to produce responses similar to text pieces written by humans.

One significant application of NLG includes summarization tools. Users could input a document into an NLG application and get a corresponding summary that does not compromise the integrity of the original text.

Earlier NLG models used templates for their text generation, filling in the blanks with related data input. However, as the technology evolved, it became more sophisticated by applying recurrent neural networks, transformers, and Markov chains. 

Nowadays, modern NLG models are capable of real-time, dynamic text generation. Some popular modern applications of NLG include chatbots, automated image captions, and AI writing tools.

Importance of Natural Language Processing

Language is how people communicate. Businesses and organizations of all sizes often have a collection of data in the form of text or written language. However, analyzing text in these formats using traditional methods can be difficult.

Natural language processing allows a more systematic understanding of the information contained in human language. This capability opens up many possibilities in various fields, such as medicine, business, education, etc.

NLP technology has evolved in ways that have made it integral to various processes. For example, social media and online advertising have become easier through chatbots and AI caption and text generators.

Digital assistants such as Apple's Siri and Amazon's Alexa also use NLP to understand user queries and provide relevant answers. Google uses NLP to improve search results. Social media sites also use this technology to detect and filter hate speech on their platform.

As AI and machine learning technologies evolve, so will the scope of NLP tasks. Researchers and scientists are still finding new ways to improve the technology and shake off some of its current issues.

Uses of Natural Language Processing

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NLP has evolved significantly through the years and has become a primary driving force of machine intelligence in many real-world applications. It has several use cases in language-related tasks, such as the ones below.

Sentiment analysis

Sentiment analysis pertains to the process of identifying the emotional tone conveyed in a text. Typically, this process entails taking a particular piece of text as input. Then, the NLP model calculates the likelihood that the sentiment expressed in that text is either positive, negative, or neutral. 

This application of NLP has applications in e-commerce and social media. You can use sentiment analysis to classify customer or client reviews and even identify signs of mental illness through social media comments. Social media sentiment analysis is an excellent way to gauge public opinions on specific topics.

Within sentiment analysis, there is a subfield known as toxicity classification. This area focuses on classifying hostile intentions and categorizing them as threats, insults, offensive language, and discrimination against particular identities.

When conducting toxicity classification, the model takes text as input and provides the likelihood of each toxicity category as its output. 

Toxicity classification models serve important purposes like moderating and enhancing online discussions. It aids in identifying and suppressing offensive remarks, recognizing hate speech, and screening documents for defamatory content.

Spam detection

Many spam detection technologies utilize NLP's text analysis capabilities to examine emails for language patterns commonly associated with spam or phishing attempts. 

These patterns could include excessive financial jargon, noticeable grammatical errors, misspelled company names, threatening language, undue urgency, etc. Spam detectors take an email's content into account, as well as additional information like the subject line and sender's identity. 

Email service providers use spam detection NLP models to enhance the user experience by identifying spam emails and segregating them into separate folders.

Machine translation

Machine translation is the automated process of translating text from one language to another. In this process, you provide a model with text in a defined source language. Then, it generates the corresponding text in a specified target language.

Google Translate stands out as one of the most widely used mainstream examples of machine translation. These models play a significant role in enhancing communication between people on various online platforms, including social media

Effective translation aims to capture the desired impact and intent shown in the source language. While today's machine translation technologies aren't perfect, they have been able to evolve in terms of accuracy. 

Named entity recognition

Named entity recognition (NER) is a process designed to identify and categorize entities within a given text. A NER model places these entities into predefined groups such as personal names, organizations, places, and numerical values. 

Typically, the input for such a model consists of text, and the output includes the identified named entities along with their respective starting and ending positions.

NER has several practical applications, such as research, summarizing documents and articles, and helping counter misinformation.

Topic modeling

Topic modeling is an unsupervised statistical and text-mining technique used to discover abstract topics within a collection of documents. 

It scans the text within the documents and detects frequently used words and phrases. These words and phrases are then grouped into topics, providing a comprehensive summary of the documents input into the model.

One widely used topic modeling technique is Latent Dirichlet Allocation (LDA). This method treats a document as a composition of multiple topics and each topic as a collection of associated words.

Currently, topic modeling has practical applications, particularly in the legal field. It aids lawyers in the identification of relevant evidence within legal documents.

Text generation

Text generation, formally known as natural language generation (NLG), produces text closely resembling human-written content. Developers and scientists can adjust these models to generate text in different styles and formats, including tweets, blog posts, and even computer code.

One of today's most popular applications of text generation is AI writing tools. Users only need to ask a question or type in a prompt, and the model provides a coherent answer resembling text written by a human.

Other prominent applications of text generation in NLP include autocomplete systems and chatbots.

Autocomplete is a technology that predicts the next word in a sequence. You can see its applications in messaging apps like WhatsApp. It's also a prominent feature in search engines like Google, where it helps predict search queries.

On the other hand, chatbots automate one side of a conversation, with the other participant being a human. These bots often have applications in business and customer support, representing the company in interactions with customers or clients.

There are two primary types of chatbots, which are:

  • Database query chatbots. These systems use a database with questions and corresponding answers, allowing users to communicate using natural language.
  • Conversation generation chatbots. These chatbots can simulate human-like dialogues and question-answering. Some can even engage in extensive conversations. 

Grammatical error correction

Grammatical error correction models incorporate grammatical rules to rectify these errors in text. 

These models approach grammatical correction as a sequence-to-sequence task. The system inputs a potentially ungrammatical sentence, processes it, and produces a grammatically correct sentence as output.

Grammarly is one of the most well-known examples of tools using this NLP system. This platform, alongside other online grammar checkers, helps writers improve their written pieces and provide better writing experiences.

Word processors like Microsoft Word also use grammatical error correction tools. Other institutions like schools also use these systems to assess and grade student essays and written works.

How Natural Language Processing Works

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NLP models find and understand relationships between different language components, such as letters, words, and sentences. 

To do this, NLP models use various techniques for data preprocessing, feature extraction, and modeling. Here are some of the methods and technologies used in natural language processing:

Data preprocessing

Data preprocessing is a crucial step that occurs before a model can effectively handle text for a particular task. It aims to enhance model performance and convert characters and words into a format the model can understand.

Some techniques used in data preprocessing include the following:

  • Parsing. This technique refers to the grammatical analysis of a sentence or piece of text. It identifies parts of speech and the syntactic relations between these different components. Although this step may seem simplistic, it is essential to ensure that more complex processes down the line work properly.
  • Stemming. Stemming is an informal technique that involves transforming words into their base forms using heuristic rules. For example, words like barks, barked, barking, and barker could be mapped to the base form of "bark." However, this technique may not be completely accurate, as other words may have similar spelling patterns but mean different things.
  • Lemmatization. Lemmatization is a similar process to stemming but with a more structured approach. This technique identifies a word's roots by analyzing its morphology with reference to a dictionary.
  • Sentence segmentation. Sentence segmentation involves dividing a lengthy text into coherent linguistic sentence units. This task may seem straightforward in some languages like English, where a period, question mark, or exclamation point often denotes the end of a sentence.

However, this process becomes more complicated depending on the text or the language used. Periods can denote the end of a sentence, but they are also used in abbreviations. Some languages, like Chinese and Mayan, do not use Western punctuation marks.

  • Stop word removal. The objective of this approach is to eliminate frequently occurring words that contribute minimal information to the text. Examples of these words include "a," "an," "the," and other similar terms.
  • Tokenization. This technique involves breaking down text into individual words and word components. The outcome typically includes a word index and tokenized text, where words are often represented as numerical tokens. 

Various deep learning methods use these numerical tokens in their processes. Techniques instructing language models to disregard less significant tokens can enhance efficiency.

Feature extraction

Many machine learning techniques use features, which are numerical representations that characterize a document within the context of the larger corpus to which it belongs. 

Feature extraction often uses methods like Bag-of-Words, TF-IDF, or general feature engineering techniques, which might involve considerations such as document length, metadata, and word polarity. 

Here is a quick overview of some widely used feature extraction techniques:

  • Bag-of-words. This technique involves tallying the occurrences of individual words or n-grams (combinations of n words) within a document.
  • TF-IDF. In bag-of-words, we count the frequency of each word or n-gram that appears in a text. In TF-IDF, we weigh and consider each word or n-gram's importance.

TF stands for Term Frequency or how often a word appears in the document. You calculate it by dividing the number of times a word is used by the document's total word count. It answers the question, "How important is this specific word in the entire document?”

On the other hand, IDF means Inverse Document Frequency. To calculate the IDF of a word or n-gram, we can use the following formula: IDF(word)=log(number of documents in the corpus/number of documents including the word).

IDF helps identify the importance of a particular word in the entire collection or corpus of documents. We multiply the TF and IDF of a word or n-gram to get its TF-IDF score.


After preprocessing the text data and identifying its features, we can feed it to an NLP architecture that can model the data to perform and accomplish specific tasks.

The features extracted from the previously discussed techniques could be fed into gradient-boosted trees, decision trees, naive Bayes, and logistic regression.

Deep neural networks can work without needing feature extraction but can also use bag-of-words or TF-IDF features as input.

NLP Languages, Libraries, and Frameworks

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Developers and computer scientists can use various programming languages and tools for natural language processing. Most programming languages, libraries, and frameworks can support NLP. Here are some of the most popular ones:


Python is the most well-used programming language for natural language processing. Using it is an excellent choice, as many developers and computer scientists have developed frameworks and libraries that accommodate NLP-related tasks.

Libraries and frameworks under this programming language include the following:

  • Natural Language Toolkit (NLTK). This toolkit is one of the first and most widely used NLP libraries written for Python. NLTK is an open-source collection of programs, libraries, and educational resources for building NLP models and programs.

It contains libraries for subtasks needed to develop NLP models, such as sentence parsing, stemming, lemmatization, word segmentation, and tokenization. It also includes libraries for tasks like semantic reasoning or the ability to deduce logical conclusions based on the text input.

  • spaCy. spaCy is another open-source NLP library for Python. It boasts support for over 66 different languages and offers pre-trained word vectors. It can also implement various well-known models, including BERT

This library can help construct robust, production-ready systems for various NLP tasks. These tasks include named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and more.

  • Deep learning libraries. Deep learning libraries like TensorFlow and PyTorch help create NLP models that support features like automatic differentiation.


Early NLP models used the R programming language. Data scientists and statisticians still use this language. Libraries and frameworks under this language include the following:

  • TidyText
  • Word2Vec
  • Weka

Issues and Concerns Regarding Natural Language Processing

NLP is still a relatively new technology, and various parties have raised concerns about its usage and development. There are questions about the models themselves and their outputs, as well as the technology's impact on society.

Environmental impact

The operation of large language models demands a substantial amount of energy for both their training and inference processes. 

One study found that training a single large language model can result in emissions of carbon dioxide equivalent to five times that of a typical automobile over its entire functional life. 

Researchers have put forth suggestions like leveraging cloud servers situated in regions abundant in renewable energy as a means to mitigate this environmental impact. Other recommendations include prioritizing computationally efficient hardware and algorithms.

High cost

The hardware and computational resources needed to produce large-scale NLP models could be too costly for smaller companies and organizations. As a result, smaller research teams do not have access to use and explore this technology. 

Some experts worry that these high costs could hinder capable scientists and engineers from contributing to this field of innovation.


Scientists use existing human data to train NLP models. Large, uncurated, and unstructured data sets have the tendency to contain social biases and inaccuracies that may affect the NLP model's outputs.

A 2021 paper explores and critiques these risks. The researchers suggest better care and curation in selecting data sets for training NLP models. They also advocate for properly evaluating a model's potential impact before starting its development.

Pro Tip: NLP text generators can offer valuable insights and can be helpful for idea generation. However, it's important to understand their limits and biases to avoid inaccuracies and misinformation.

Embrace Big Data Through Natural Language Processing

Natural language processing is an exciting new technology with several practical applications for businesses and organizations. 

It is an excellent tool to better understand big sets of textual information, such as public sentiment and customer preferences. It also has the potential to make internal processes more efficient.

Through the adoption of NLP and AI, businesses of all sizes can access user-friendly and beneficial tools that can aid their everyday operations. 

Archive provides users with state-of-the-art AI tools to help businesses connect with their clients and customers through user-generated content. It helps companies organize their social media and marketing efforts more efficiently and achieve set goals.

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FAQs on Natural Language Processing

What are some popular NLP models?

Many NLP models have significantly impacted the AI community as well as gained mainstream attention. These models include the following:

  • GPT-4 (Generative Pre-trained Transformer 4) 
  • BERT: Bidirectional Encoder Representations from Transformers
  • T5: Text-to-Text Transfer Transformer

When did research for NLP begin?

The roots of NLP began in the 1950s when Alan Turing developed the Turing Test. This test determines whether or not a computer is intelligent. One criterion of machine intelligence is the capability for automated interpretation and natural language generation.

What is the difference between machine learning and NLP?

NLP is a field that falls under machine learning. Machine learning methods generally focus on developing models that can learn automatically and function without human intervention. NLP is more specific, focusing on enabling machines to comprehend and generate human-like written text.

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