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Natural Language Processing & Machine Learning: An Introduction

This article describes how machine learning can interpret natural language processing and why a hybrid NLP-ML approach is highly suitable.

Natural Language Processing & Machine Learning: An Introduction


Natural language processing is an aspect of everyday life, and in some applications, it is necessary within our home and work. For example, without providing too much thought, we transmit voice commands for processing to our home-based virtual home assistants, smart devices, our smartphones - even our personal automobiles.

Voice-enabled applications such as Siri, Alexa, and Google Assistant use natural language processing - combined with machine learning - to give us answers to our questions, add items to our personal calendars and call our contacts using voice commands. Consequently, natural language processing is making our lives more manageable and revolutionizing how we live, work, and play.

What Precisely is Natural Language Processing?

Natural language processing refers to the technical branch of computer science, or artificial intelligence, that is concerned with providing computer systems with the ability to comprehend digital text and spoken remarks - in much the same way that human beings can accomplish.

Natural language processing combines computational linguistics, or the rule-based modeling of human languages, statistical modeling, machine-based learning, and deep learning benchmarks. Jointly, these advanced technologies enable computer systems to process human languages via the form of voice or text data. The desired outcome or purpose is to 'understand' the full significance of the respondent's messaging, alongside the speaker or writer's objective and belief.

Natural language processing operates within computer programs to translate digital text from one language to another, to respond appropriately and sensibly to spoken commands, and summarise large volumes of information. All within real-time processing boundaries.

More than likely you have already had interaction with natural language processing in the form of voice-guided GPS car systems, personal digital assistants, dictation software suing speech-to-text algorithms, customer service online chatbots, and other consumer amenities.

Natural language processing also has a significant (and growing) role within enterprise-based solutions that support streamlining business operations, increase and augment employee productivity, and facilitate mission-critical business system processes.

Key Differences - Natural Language Processing and Machine Learning

Natural language processing, artificial intelligence, and machine learning are occasionally used interchangeably, however, they have distinct definition differences. Artificial intelligence is an encompassing or technical umbrella term for those smart machines that can thoroughly emulate human intelligence. Natural language processing and machine learning are both subsets of artificial intelligence.

Artificial intelligence is a detailed component of the wider domain of computer science that facilitates computer systems to solve challenges previously managed by biological systems. Artificial intelligence has many applications within today's society.

Natural language processing is a subset of artificial intelligence that presents machines with the ability to read, understand and analyze the spoken human language. With natural language processing, machines can assemble the meaning of the spoken or written text, perform speech recognition tasks, sentiment or emotion analysis, and automatic text summarization.

Machine Learning is an application of artificial intelligence that equips computer systems to learn and improve from their experiences without being explicitly and automatically programmed to do so. Machine learning machines can help solve AI challenges and enhance natural language processing by automating language-derived processes and supplying accurate answers.

How Natural Language Processing and Machine Learning is Applied

Natural language processing-based solutions comprise language translation, digital speech recognition, emotive or sentiment analysis, question and answer online systems, smart chatbot systems, automatic text summarization capabilities, financial market intelligence, automatic text categorization, and automatic language grammar scanning.

These technologies help both individuals and organizations to analyze their data, uncover new insights, automate time and labor-consuming processes and gain competitive advantages.

Language Translation

Translating languages is a far more intricate process than simply translating using word-to-word replacement techniques. Each language has its own unique grammar rules and limitations. The challenge of translating any language passage or digital text is to perform this process without changing the underlying style or meaning. As computer systems cannot explicitly understand grammar, they require a specific program to dismantle a sentence, then reassemble using another language in a manner that makes sense to humans.

Google Translate is such a tool, a well-known online language translation service. Previously Google Translate used a Phrase-Based Machine Translation, which scrutinized a passage for similar phrases between dissimilar languages. Presently, Google Translate uses the Google Neural Machine Translation instead, which uses machine learning and natural language processing algorithms to search for language patterns.

Speech Recognition Activities

Speech recognition capabilities are a smart machine's capability to recognize and interpret specific phrases and words from a spoken language and transform them into machine-readable formats. It uses natural language processing algorithms to allow computers to imitate human interactions, and machine language methods to reply, therefore mimicking human responses.

Google Now, Siri, and Alexa are a few of the most popular models utilizing speech recognition technology. By simply saying 'call Fred', a smartphone mobile device will recognize what that personal command represents and will then create a call to the personal contact saved as Fred.

Emotion and Sentiment Analysis

Sentiment or emotive analysis uses both natural language processing and machine learning to decode and analyze human emotions within subjective data such as news articles and influencer tweets. Positive, adverse, and impartial viewpoints can be readily identified to determine the consumer's feelings towards a product, brand, or a specific service. Automatic sentiment analysis is employed to measure public or customer opinion, monitor a brand's reputation, and further understand a customer's overall experience.

Financial markets are sensitive domains heavily influenced by human sentiment and emotion. Negative presumptions can lead to stock prices dropping, while positive sentiment could trigger investors to purchase more of a company's stock, thereby causing share prices to rise.

Online Smart Chatbots

Online chatbots are computer programs that provide 'smart' automated explanations to common consumer queries. They contain automated pattern recognition systems with a rule-of-thumb response mechanism. They are used to conduct worthwhile and meaningful conversations with people interacting with a particular website. Initially, chatbots were only used to answer fundamental questions to minimize call center volume calls and deliver swift customer support services.

However, nowadays, AI-powered chatbots are developed to manage more complicated consumer requests making conversational experiences somewhat intuitive. For example, chatbots within healthcare systems can collect personal patient data, help patients evaluate their symptoms, and determine the appropriate next steps to take. Additionally, these healthcare chatbots can arrange prompt medical appointments with the most suitable medical practitioners, and even suggest worthwhile treatments to partake.

Intelligent Question and Answer Systems

Question and answer computer systems are those intelligent systems used to provide specific answers to consumer queries. Besides chatbots, question and answer systems have a large array of stored knowledge and practical language understanding algorithms - rather than simply delivering 'pre-canned' generic solutions. These systems can answer questions like 'When did Winston Churchill first become the British Prime Minister?' or 'How do I proceed on foot to Paris Gare du Nord?'. These intelligent responses are created with meaningful textual data, along with accompanying audio, imagery, and video footage.

Question and answer smart systems are found within social media chatrooms using intelligent tools such as IBM's Watson.

Automatic Text Condensing and Summarisation

Automatic text condensing and summarization processes are those tasks used for reducing a portion of text to a more succinct and more concise version. This process happens by extracting the main concepts and preserving the precise meaning of the content. This application of natural language processing is used to create the latest news headlines, sports result snippets via a webpage search and newsworthy bulletins of key daily financial market reports.

Financial Market Intelligence

Financial market intelligence gathers valuable insights covering economic trends, consumer spending habits, financial product movements along with their competitor information. Such extractable and actionable information is used by senior business leaders for strategic decision-making and product positioning. Market intelligence systems can analyze current financial topics, consumer sentiments, aggregate, and analyze economic keywords and intent. All processes are within a structured data format that can be produced much quicker than traditional desk and data research methods.

By utilizing market intelligence services, organizations can identify those end-user search queries that are both current and relevant to the marketplace, and add contextually appropriate data to the search results. As a result, it can provide meaningful information to help those organizations decide which of their services and products to discontinue or what consumers are currently targeting.

Automatic Text Classification

The process required for automatic text classification is another elemental solution of natural language processing and machine learning. It is the procedure of allocating digital tags to data text according to the content and semantics. This process allows for immediate, effortless data retrieval within the searching phase. This machine learning application can also differentiate spam and non-spam email content over time.

Automatic Grammar Checking and Suggestive Alternatives

Automatic grammar checking, which is the task of noticing and remediating grammatical language errors and spelling mistakes within the text, is another prominent component of NLP-ML systems. Auto-grammar checking processes will visually warn stakeholders of a potential error by underlining an identified word in red.

Advantages, Disadvantages of Natural Language Processing and Machine Learning

Like further technical forms of artificial intelligence, natural language processing, and machine learning come with advantages, and challenges.

Advantages of NLP include the following:

Once successfully implemented, using natural language processing/ machine learning systems becomes less expensive over time and more efficient than employing skilled/ manual labor.

Natural language processing assists businesses to offer more immediate customer service with improved response times. Regardless of the time of day, both customers and prospective leads will receive direct answers to their queries.

Pretrained machine learning systems are widely available for skilled developers to streamline different applications of natural language processing, making them straightforward to implement.

While advances within natural language processing are certainly promising, there are specific challenges that need consideration.

Disadvantages of NLP include the following:

The training and development of new machine learning systems can be time-consuming, and therefore expensive. If a new machine learning model is required to be commissioned without employing a pre-trained prior version, it may take many weeks before a minimum satisfactory level of performance is achieved.

Another challenge for natural language processing/ machine learning is that machine learning is not fully-proof or 100 percent dependable. Automated data processing always incurs a possibility of errors occurring, and the variability of results is required to be factored into key decision-making scenarios.

NLP Tools and Approaches

Python and the Natural Language Toolkit

The Python programing language provides a wide range of online tools and functional libraries for coping with all types of natural language processing/ machine learning tasks. The majority of these tools are found in Python's Natural Language Toolkit, which is an open-source collection of functions, libraries, programs, and educational resources for designing and building NLP/ ML programs.

The Natural Language Toolkit includes libraries for sentence language parsing, keyword segmentation, language lemmatization and stemming (these are methods for 'trimming' words right down to their basic language roots), and tokenization (which is used for splitting phrases, sentences, paragraphs, and passages into smaller tokens that allow the computer programs to understand the digital text components).

Statistical NLP and Machine Learning

The earliest natural language processing/ machine learning applications were hand-coded by skilled programmers, utilizing rules-based systems to perform certain NLP/ ML functions and tasks. However, they could not easily scale upwards to be applied to an endless stream of data exceptions or the increasing volume of digital text and voice data.

Statistical NLP/ ML combines both computer algorithms with machine learning and deep learning automated models to systematically extract, compartmentalize, and digitally label text and voice data segments - and then allocate a statistical probability to each possible meaning of those segments.

Natural Language Processing/ Machine Learning Applications - by Industry

Natural language processing and machine learning systems have only commenced their commercialization journey within industries and business operations. The following examples are just a few of the most common - and current - commercial applications of NLP/ ML in some of the largest industries globally.

Healthcare and Medical

Natural language processing is starting to aid the healthcare and medical fields, and its potential applications are countless. Currently, NLP/ ML is helping researchers and scientists combat the COVID-19 pandemic in various ways, by analyzing incoming emails and live chatbot patient data from online medical helplines. These systems can identify and flag those people with likely COVID-19 symptoms. This automated process has permitted physicians to proactively prioritize their patients and bring those in need of immediate care into the hospital system much quicker.

NLP/ ML systems also allow medical providers to quickly and accurately summarise, log and utilize their patient notes and information. They use text summarization tools with named entity recognition capability so that normally lengthy medical information can be swiftly summarised and categorized based on significant medical keywords. This process helps improve diagnosis accuracy, medical treatment, and ultimately delivers positive patient outcomes.

Financial Institutions - FinTech

Using emotive NLP/ ML analysis, financial institutions can analyze larger amounts of meaningful market research and data, thereby ultimately leveraging real-time market insight to make informed investment decisions. This process also has a positive impact on risk management activities.

NLP/ ML helps banks and other financial security institutions to identify money laundering activities or other fraudulent circumstances.


Natural language processing/ machine learning systems are leveraged to help insurers identify potentially fraudulent claims. Using deep analysis of customer communication data - and even social media profiles and posts - artificial intelligence can identify fraud indicators and mark those claims for further examination.

Insurers utilize text mining and market intelligence features to 'read' what their competitors are currently accomplishing. They can subsequently plan what products and services to bring to market to attain or maintain a competitive advantage.

Manufacturing, Production Line, and Supply Chain

To improve their manufacturing pipeline, NLP/ ML systems can analyze volumes of shipment documentation and give manufacturers deeper insight into their supply chain areas that require attention. Using this data, they can perform upgrades to certain steps within the supply chain process or make logistical modifications to optimize efficiencies.

Manufacturers leverage natural language processing capabilities by performing web scraping activities. NLP/ ML can "web scrape" or scan online websites and webpages for resources and information about industry benchmark values for transport rates, fuel prices, and skilled labor costs. This automated data helps manufacturers compare their existing costs to available market standards and identify possible cost-saving opportunities.

Consumer Retail

Retailers can leverage consumer sentiment analysis. By analyzing customer opinion and their emotions towards their brands, retail companies can initiate informed decisions right across their business operations. NLP/ ML systems leverage social media comments, customer reviews on brands and products, to deliver meaningful customer experience data. Retailers use such data to enhance their perceived weaknesses and strengthen their brands.

NLP/ ML systems also improve customer loyalty by initially enabling retailers to understand this concept thoroughly. By analyzing their profitable customers' communications, sentiments, and product purchasing behavior, retailers can understand what actions create these more consistent shoppers, and provide positive shopping experiences.


The most important component required for natural language processing and machine learning to be truly effective is the initial training data. Once enterprises have effective data collection techniques and organization-wide protocols implemented, they will be closer to realizing the practical capabilities of NLP/ ML.

NLP/ ML systems work tirelessly behind the scenes to improve computer tools and smart devices we use daily: such as a spell-checker, an online chatbot, or Google language translators.

Machine learning and natural language processing models have been highly topical subjects within all major industries in recent years and can be considered a new standard to attain within artificial intelligence and technical-scientific research.

Completely integrated with machine learning algorithms, natural language processing creates automated systems that learn to perform intricate tasks by themselves - and achieve higher success rates through experience. Natural language processing-powered devices can help an individual extract a specific entity from a personal email or create considerable financial efficiencies for a manufacturer that utilizes NLP/ ML for production line fault identification while directly observing a moving production line.

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