What Is NLP? Natural Language Processing Explained

Natural language processing is a branch of AI that enables computers to understand, process, and generate language just as people do — and its use in business is rapidly growing.

Natural language processing definition

Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all

While the term originally referred to a system's ability to read, it's since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) — a computer's ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language.

The introduction of transformer models in the 2017 paper "Attention Is All You Need" by Google researchers revolutionized NLP, leading to the creation of generative AI models such as Bidirectional Encoder Representations from Transformer (BERT) and subsequent DistilBERT — a smaller, faster, and more efficient BERT — Generative Pre-trained Transformer (GPT), and Google Bard.

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How natural language processing works

NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they're processed using grammatical rules, people's real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next. For example, a translation algorithm that recognizes that, in French, "I'm going to the park" is "Je vais au parc" will learn to predict that "I'm going to the store" also begins with "Je vais au." All the algorithm then needs is the word for "store" to complete the translation task.

NLP applications

Machine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're helping to train the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future.

Chatbots work the same way. They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like "Hey, Alexa." That's why critics say these programs are always listening; if they weren't, they'd never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.

Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.

Rajeswaran V, senior director at Capgemini, notes that Open AI's GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says.

NLP systems that rely on transformer models are especially strong at NLG.

Natural language processing examples

Data comes in many forms, but the largest untapped pool of data consists of text — and unstructured text in particular. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:

Edmunds drives traffic with GPT: The online resource for automotive inventory and information has created a ChatGPT plugin that exposes its unstructured data — vehicle reviews, ratings, editorials — to the generative AI. The plugin enables ChatGPT to answer user questions about vehicles with its specialized content, driving traffic to its website.

Eli Lilly overcomes translation bottleneck: With global teams working in a variety of languages, the pharmaceutical firm developed Lilly Translate, a home-grown NLP solution, to help translate everything from internal training materials and formal, technical communications to regulatory agencies. Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems.

Accenture uses NLP to analyze contracts: The company's Accenture Legal Intelligent Contract Exploration (ALICE) tool helps the global services firm's legal organization of 2,800 professionals perform text searches across its million-plus contracts, including searches for contract clauses. ALICE uses "word embedding" to go through contract documents paragraph by paragraph, looking for keywords to determine whether the paragraph relates to a particular contract clause type.

Natural language processing software

Whether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:

Natural Language Toolkit (NLTK), an open-source framework for building Python programs to work with human language data. It was developed in the Department of Computer and Information Science at the University of Pennsylvania and provides interfaces to more than 50 corpora and lexical resources, a suite of text processing libraries, wrappers for natural language processing libraries, and a discussion forum. NLTK is offered under the Apache 2.0 license.

Mallet, an open-source, Java-based package for statistical NLP, document classification, clustering, topic modeling, information extraction, and other ML applications to text. It was primarily developed at the University of Massachusetts Amherst.

SpaCy, an open-source library for advanced natural language processing explicitly designed for production use rather than research. Licensed by MIT, SpaCy was made with high-level data science in mind and allows deep data mining.

Amazon Comprehend. This Amazon service doesn't require ML experience. It's intended to help organizations find insights from email, customer reviews, social media, support tickets, and other text. It uses sentiment analysis, part-of-speech extraction, and tokenization to parse the intention behind the words.

Google Cloud Translation. This API uses NLP to examine a source text to determine language and then use neural machine translation to dynamically translate the text into another language. The API allows users to integrate the functionality into their own programs.

Natural language processing courses

There's a wide variety of resources available for learning to create and maintain NLP applications, many of which are free. They include:

NLP – Natural Language Processing with Python from Udemy. This course provides an introduction to natural language processing in Python, building to advanced topics such as sentiment analysis and the creation of chatbots. It consists of 11.5 hours of on-demand video, two articles, and three downloadable resources. The course costs $94.99, which includes a certificate of completion.

Data Science: Natural Language Processing in Python from Udemy. Aimed at NLP beginners who are conversant with Python, this course involves building a number of NLP applications and models, including a cipher decryption algorithm, spam detector, sentiment analysis model, and article spinner. The course consists of 12 hours of on-demand video and costs $99.99, which includes a certificate of completion.

Natural Language Processing Specialization from Coursera. This intermediate-level set of four courses is intended to prepare students to design NLP applications such as sentiment analysis, translation, text summarization, and chatbots. It includes a career certificate.

Hands On Natural Language Processing (NLP) using Python from Udemy. This course is for individuals with basic programming experience in any language, an understanding of object-oriented programming concepts, knowledge of basic to intermediate mathematics, and knowledge of matrix operations. It's completely project-based and involves building a text classifier for predicting sentiment of tweets in real-time, and an article summarizer that can fetch articles and find the summary. The course consists of 10.5 hours of on-demand video and eight articles, and costs $19.99, which includes a certificate of completion.

Natural Language Processing in TensorFlow by Coursera. This course is part of Coursera's TensorFlow in Practice Specialization, and covers using TensorFlow to build natural language processing systems that can process text and input sentences into a neural network. Coursera says it's an intermediate-level course and estimates it will take four weeks of study at four to five hours per week to complete.

NLP salaries

Here are some of the most popular job titles related to NLP and the average salary (in US$) for each position, according to data from PayScale.

Computational linguist: $60,000 to $126,000

Data scientist: $79,000 to $137,000

Data science director: $107,000 to $215,000

Lead data scientist: $115,000 to $164,000

Machine learning engineer: $83,000 to $154,000

Senior data scientist: $113,000 to $177,000

Software engineer: $80,000 to $166,000

6 Ways AI Can Revolutionize Digital Forensics

Digital forensics plays a crucial role in today's cyber-driven world, enabling investigators to unravel complex cybercrimes and gather evidence. With the growing volume and complexity of digital data, integration of artificial intelligence (AI) into digital forensics technology has become essential. AI algorithms and techniques offer a range of benefits, enhancing the efficiency and effectiveness of investigations and helping organizations proactively mitigate data-based security risks.

From automated log analysis and malware detection to network traffic analysis and forensic triage, AI can play a crucial role in several digital forensic activities and have a transformative impact on investigations.

Here are six ways AI can revolutionize digital forensics right now.

1. Automated Log Analysis

Security teams often deal with a massive volume of log files generated by various systems, applications, and network devices, but analyzing these logs manually can be time-consuming and error-prone. That's where automated log analysis comes in.

AI algorithms excel at processing vast quantities of log files and analyzing them for patterns and anomalies. With AI-powered log analysis, investigators can swiftly identify suspicious activities, potential security incidents, and areas requiring further investigation. AI enhances the speed and accuracy of log analysis, enabling investigators to focus their efforts on relevant areas of interest and avoid spending time and resources on manual review.

2. Malware Detection

The rapid evolution of malware calls for advanced detection methods. AI-powered malware detection systems leverage machine learning to review and scan code and study user behavior patterns, detecting malicious software more effectively and helping investigators remove malware from compromised systems to safeguard against further attacks.

For instance, security companies employ AI algorithms to continuously learn from known malware samples and their characteristics. By training these algorithms on large datasets, they can detect and classify new and previously unknown malware strains based on similarities to previously identified threats and flag a potential attack before it happens.

3. Image and Video Analysis

The analysis of digital images and videos is a critical component of digital forensics. For example, AI algorithms can sift through large volumes of multimedia content — quickly identifying faces, objects, or text within images and videos, thus significantly speeding up the process of finding and extracting crucial evidence — and support a wide range of investigation scenarios.

Consider a case where investigators need to identify a suspect captured in surveillance footage from a crowded area. Reviewing video footage is often tedious and can take hours. AI-powered facial recognition technology can rapidly scan through vast amounts of video data, pinpointing individuals of interest and significantly reducing the manual effort required. This technology expedites the identification process, enabling investigators to focus their efforts on the most relevant leads and accelerate the progress of the investigation.

4. Natural Language Processing

AI technologies like natural language processing (NLP) enable the analysis of pertinent information from large volumes of text data. For example, text-based data, including emails, chat logs, and documents, often contain valuable evidence in digital investigations. Using extractive AI can be more efficient and accurate to uncover relationships, detect patterns, and identify key individuals during text-focused investigations.

Imagine a scenario where investigators are examining a massive collection of chat logs to identify potential collaborators in a cybercrime. AI-powered NLP algorithms can rapidly process and analyze the text data, identifying recurring phrases, suspicious patterns, and connections between individuals. This enables investigators to pinpoint key persons of interest and uncover hidden networks, expediting the investigative process and enabling timely interventions.

5. Network Traffic Analysis

Monitoring and analyzing network traffic patterns is essential for detecting and responding to cyberattacks. Rather than conducting a manual audit and analyzing network traffic patterns at predetermined intervals, forensics teams can train AI algorithms to analyze network packets automatically, identify deviations from normal traffic patterns, and issue alerts when an anomaly merits further investigation. AI can also assist in correlating network events with known attack patterns, providing valuable insights for incident response teams.

6. Forensic Triage

Digital investigations involve massive volumes of data, requiring investigators to quickly sift through and prioritize relevant evidence. AI in forensic triage often involves the use of machine learning algorithms to classify and categorize large numbers of digital files based on their relevance to an investigation. These tools analyze file metadata, content, and other attributes to prioritize files for closer scrutiny, continually "learning" to identify relevant material with increasing accuracy as new data is added to the investigation. Forensics teams are able to quickly identify and focus on the most important evidence earlier, leading to faster and more effective investigations while optimizing resource allocation.

The Future of AI in Digital Forensics

The integration of AI into digital forensics technology has become an important topic of conversation among experts in the field. The potential applications of AI to forensics will continue to expand, with particular emphasis on AI's advanced capabilities in data analytics, pattern recognition, and anomaly detection.

It will also become increasingly important to determine accuracy in the age of AI, and many may consider the integration of multifactor authentication, such as fingerprint or eye authentication. However, challenges related to data privacy, bias, and accuracy must be carefully addressed to ensure the use of AI in digital forensics is always ethical and responsible.

Natural Language Processing Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028)

ReportLinker

The NLP Market size is estimated at USD 25.62 billion in 2023, and is expected to reach USD 75.01 billion by 2028, growing at a CAGR of 23.97% during the forecast period (2023-2028). Key Highlights.

New York, Aug. 18, 2023 (GLOBE NEWSWIRE) -- Reportlinker.Com announces the release of the report "Natural Language Processing Market Size & Share Analysis - Growth Trends & Forecasts (2023 - 2028)" - https://www.Reportlinker.Com/p06483794/?Utm_source=GNWThe NLP Market size is estimated at USD 25.62 billion in 2023, and is expected to reach USD 75.01 billion by 2028, growing at a CAGR of 23.97% during the forecast period (2023-2028).

Key HighlightsOver the past few years, deep learning architectures and algorithms have made impressive advances in image recognition and speech processing. The application of natural language processing (NLP) has made significant contributions, yielding state-of-the-art results for some complex NLP application tasks. NLP has witnessed considerable growth over the past few years, owing to the affordable, scalable, and computational power, increased digitization of data, and the merger of NLP with deep learning (DL) and machine learning (ML).The increasing use of NLP in healthcare and call centers, rising demand for advanced text analytics, and growing machine-to-machine technology are the primary factors responsible for the market studied.NLP is witnessing growth in healthcare centers, as many organizations use the technology to ingest and analyze massive amounts of patient data. Additionally, the growing use of the internet and connected devices, along with the enormous volume of patient data, is driving the growth of the market studied. Apart from this, the rapidly increasing data security issues, along with the minimal accessibility of NLP-based software among organizations, are hampering industry expansion.During the pandemic, the market witnessed high growth in the healthcare sector. For instance, in response to the COVID-19 pandemic, the White House and a coalition of leading research groups prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 200,000 scholarly articles, including over 100,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset was provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights supporting the fight against this infectious disease.

Natural Language Processing Market Trends

Large Organizations Expected to Register Significant Growth

Large organizations are one of the primary drivers and investors in the NLP market. As these organizations were increasingly adopting deep learning and supervised and unsupervised machine learning technologies for various applications, the adoption of NLP is likely to increase. Cost and risk are some of the major factors driving the adoption of these technologies among large organizations.Most large end-user organizations across various industries mainly utilize these technologies to enhance their internal and external operations. Moreover, the ROI of the technology is only sometimes in the monetary form; hence, most small organizations find it risky to invest.Moreover, large-scale social media platforms also utilize text analytics and NLP technologies to monitor and track social media activities, such as political reviews and hate speeches. Platforms like Facebook and Twitter are managing published content with the help of these tools. The demand for information extraction product applications is also anticipated to increase due to the growing importance of web data for effective marketing and decision-making. Within the next few years, mobile chatbots are anticipated to revolutionize the marketing and commerce sectors.In September 2022, Lexicala, a language content provider, introduced new multilingual lexical data solutions for the Language Technology sector and academic research. Domain categorization, text annotation, expert parallel corpora, morphology, and other premium cross-lingual resources are included in the solutions for natural language processing (NLP) applications.

North America to Witness the Highest Market Growth

According to RichRelevance, online shoppers in the United States are more inclined to use Google Assistant than other platforms. Additionally, the utilization of smart speakers to obtain results on diverse topics is gaining increasing popularity, which, in turn, is expected to compel companies to invest in the technology. This creates an immense opportunity for other application developers to target potential customers.NLP may also enhance the customer experience programs with various added benefits, thereby attracting more consumers, which, in turn, is projected to have a positive impact on the market growth in the country. The growing innovations in the market studied by the global players based in the United States are bringing advancement into the NLP market and fueling the rate of product launches in the region.For instance, IBM Research AI is exploring various applications of NLP for enterprise domains. For this, the company developed three programs, the first being advanced AI, where systems can learn from fewer amounts of data, leverage external knowledge, and use techniques that include neuro-symbolic approaches to language, which combine neural and symbolic processing. The second program is an analyzing AI, which focuses on how a system reaches a decision. The third is scaling AI, which enables continuous adaptation, and better monitoring and testing of systems, in order to support the deployment of language systems under the rigorous expectations of enterprises.In October 2022, IBM announced a collaboration with the United States Patent and Trademark Office (USPTO) to test AI-driven intellectual property (IP) analysis tool, the IBM IP Advisor with Watson Demonstration System. The Demonstration System would assist customers in accessing useful information utilizing their own terminology by allowing natural language processing of queries with IBM Watson Discovery technology, eliminating the need for specific words and complex procedures.

Natural Language Processing Industry Overview

The Natural Language Processing Market is highly competitive and consists of several major players who have been trying to gain larger shares. These major players with prominent shares in the market have been focusing on expanding their customer base across foreign countries. They are providing new innovative solutions, along with deals and mergers, to increase their market shares and profitability. Key players include Google Inc. And Microsoft Corporation.

In October 2022, Red Hat and IBM launched Project Wisdom, a project to integrate natural language processing (NLP) into the open-source Ansible IT operations automation platform. The objective is to make IT automation more accessible to a larger spectrum of end users and IT professionals who lack the declarative programming skills required to automate IT operations using YAML files.

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