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May 27, 2024 | 10 Mins read

Understanding NLP: Your Ultimate Guide to Natural Language Processing

So how do these voice assistants actually hear you? It’s NLP. Learn more about Natural Language Processing, the science behind how machines can readily understand human speech (and human-like speech from machines!) and use it to improve interaction between humans and computers, and how it can be used to change the world.

Key Takeaways

  • NLP is the intersection of linguistics, machine learning and deep learning that allows computers to process, understand and generate human speech for improved human-machine communication.

  • Natural Language Understanding (NLU) involves deciphering intent and content from communications while Natural Language Generation (NLG) involves the automatic generation of human-readable text for any desired application.

  • Although machine learning (deep learning in particular) has recently propelled the capabilities of NLP tools and services, there remain inherent challenges with language ambiguity, data sourcing and quality, and ethical use.

Natural Language Processing (NLP) Deconstructed

Simply put, NLP teaches machines to read human language. It’s a subfield of artificial intelligence (AI) that when applied to human-machine communication, allows for more natural and efficient interaction between the two. The field focuses on the development of systems that can:

  • understand and interpret human language

  • generate human responses

  • retrieve information from text

  • summarize and classify text

  • translate text

The goal of NLP is to narrow the gap between human and machine language understanding for more efficient human-machine communication.

The Two Foundations of NLP: Understanding and Generation

As mentioned above, NLP is the intersection of linguistics, machine learning and deep learning that allows computers to process, understand and generate human speech for improved human-machine communication.


The field of NLP rests on two intersecting subfields: natural language understanding (NLU) and natural language generation (NLG). The combination of these two—using NLU to understand human language and NLG to produce text from a machine—comprise the two main pillars of NLP.

Natural Language Understanding (NLU)

Natural language understanding (NLU) focuses on extracting meaning from conversations and goes beyond simply recognizing words to identify the user’s intent.

NLU enables computers to interpret and understand what the user intends to say and what specific topics users are talking about by identifying key elements such as intent and entities (or named entities).

But NLU poses challenges—mainly because of the inherent complexities and variabilities of natural human communication. To mitigate these challenges, NLU uses parsing, a technique that allows computers to input text and break it down into structured data that the computer can understand. This includes tokenizing the input and using grammars to model the syntax and semantics of the language.

Natural Language Generation (NLG)

Natural language generation (NLG) is the complement of NLU. Instead of understanding human language, NLG involves the automatic generation of human-readable text from machines. NLG enables systems to produce human-like responses as part of a conversational interface (e.g. voice assistants, call centers, and chatbots).

But NLG doesn’t end with conversational interfaces. It can also be applied to other types of documents (e.g. news articles, sports reports, financial reports, product descriptions, or sales letters) by imitating the writing style of a human. Moreover, NLG can take complex numerical data and convert it into a simple, easy-to-understand narrative, thus automating report generation in areas such as financial forecasting, stock market trends, or weather forecasts.

Machine Learning and NLP

Role of Machine Learning in NLP

The field of NLP experienced a major breakthrough in the 1980s with the development of the first machine learning-based algorithms for language processing. Machine learning is crucial to NLP as it enables the models to learn language patterns, syntax and context, thus dramatically speeding up and improving the efficiency of NLP systems to understand human language. There are several ways that machine learning NLP interactions can take place.

Using Deep Learning Models for NLP

Deep learning is transforming the field of NLP and has the potential to teach computers how to think like humans, thus is crucial for processing data in natural language. Using deep learning models such as recurrent neural networks (RNNs) and transformers, a wide array of NLP problems can be tackled including text classification, translation and summarization.

Consider, for example, the NLP task of sentiment analysis (identifying a document’s polarity, i.e. is the sentiment positive or negative?) and named entity recognition (identifying entities in text and categorizing them into specific categories). Both tasks have been significantly improved with the application of deep learning and neural network models, with sentiment analysis in particular reaping the benefits of recent advances in this area. Recurrent neural network language models learned from large amounts of text take as input a word embedding and output a probability distribution over the possible next words. Further fine-tuning of these language models can be done by using a specific dataset.

The Training Data and NLP Algorithms

The training data strongly affects the performance of the NLP algorithms. The better the training data the higher the accuracy and performance for applications such as sentiment analysis and chatbots. The training data for NLP tasks is usually constructed by manual labeling via human annotators. This involves tagging large collections of data with named entities, parts of speech, sentiment or intents.

But NLP must also account for noise and uncertainty and ensure that the models do not reinforce any bias that may exist in the training data. Corpus-based, statistical methods have become more prevalent in NLP in recent years due to the vast amounts of text and speech data that now exist, providing rich sources of training data.

Principal NLP Tasks and Functions

NLP Techniques

NLP leverages a variety of tasks such as:

  • email classification

  • translation

  • text analytics

  • tokenization

  • POS tagging

  • named entity recognition (NER)

  • sentiment analysis

  • text classification

  • language modeling

  • machine translation

These NLP tasks use machine learning models and employ machine learning methods and allow NLP technologies to process and understand human language through a language model.

Speech Recognition and Voice Data

Speech recognition allows for the interpretation of voice data and is the core technology of NLP. It involves breaking down words into smaller segments and the training of algorithms to recognize different accents, slurs and nonstandard grammar use so that voice data can be converted to text. Smart assistants rely on speech recognition technology to interpret voice questions and provide context-related responses.

Speech recognition software converts human spoken language to a form that can be recognized by virtual assistants and business applications (e.g. for tasks such as transcription and machine translation).

Machine Translation and Human Communication

Machine translation is a key NLP function. It involves the translation of text from one language to another while preserving meaning and context. Recent advances in NLP have dramatically improved the accuracy and grammatical fidelity of machine translation between languages.

But the challenges of multilingualism and language variation remain an issue for NLP systems and present a significant problem in machine translation. For example, neural machine translation has rendered intermediate word alignment methods obsolete.

NLP Methods and Tools

Key NLP tasks

Pre-processing is a crucial technique in NLP and involves the structuring of raw text data into a form suitable for analysis. This includes tokenization, stemming and lemmatization which are the primary methods for breaking text into smaller units, reducing words to their stem or root form and removing inflectional endings (e.g. ran, runs, running all reduce to run).

Developers working on NLP applications typically rely on a number of libraries and programs such as Natural Language Toolkit (NLTK).

Computational Linguistics and Semantic Analysis

Computational linguistics delivers the linguistic analysis needed for the breakdown of human language into elements that can be understood by computers and is the technology behind Natural Language Generation (NLG).

Understanding sentence structure is a key component of computational linguistics and is performed by a range of NLP tasks such as syntactic parsing which uses chart parsing and part-of-speech (POS) tagging. POS tagging helps software determine the part of speech of each word in a sentence (e.g. noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.).

Other critical NLP tasks include word-sense disambiguation and semantic analysis. Contextual embedding, for example, is a technique used in word-sense disambiguation to find the context of a word in the corpus.

Linguistic ambiguity and polysemy (the same word having different meanings) are significant challenges in semantic analysis for computational linguistics. But recent advances have seen hybrid approaches to semantic analysis that combine symbolic processing which interprets language through rules and logical relationships, with statistical learning which estimates the probability of language phenomena.

Some popular tools include NLTK which is designed for Python, SpaCy which includes more modern algorithms and Stanford CoreNLP which is designed for Java.

An excellent resource for learning more about NLP and programming is Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit - a practical guide complete with helpful examples and exercises.

Challenges and Open Problems in NLP

Despite recent major advances in NLP, it remains a challenging field due to the fluid and sometimes vague nature of human language. Mitigating these challenges and exploring open problems in NLP will require:

  • Interdisciplinary research

  • Better computational efficiency

  • Data sourcing and annotation

  • More accurate and robust models

  • Ethical NLP research and applications

By working on these issues and exploring new methods in NLP, we can begin to realize its full potential.

Mitigating Ambiguity and Language Variation

A major challenge in NLP is mitigating ambiguity and polysemy in language (where a word or phrase may potentially have multiple meanings). But despite this, NLP is able to learn through dialects, slang and irregular grammar found in natural language.

Moreover, NLU systems can learn to overcome common human communication mistakes such as mispronunciation or improper word order. This demonstrates the potential of NLP to learn the nuances and intricacies of human language and its various forms and uses.

New Research in Machine Learning

Many new approaches are being developed to tackle NLP problems. For example, researchers are working on semi-supervised learning methods that use data augmentation techniques to alleviate the problems of data scarcity and improve the quality of datasets for NLP.

Moreover, the field of NLP has seen advances in several areas including:

  • Rich language models

  • Contextualized models

  • Improved context modeling

  • Robustness of NLP systems

  • Models for handling uncertainty

  • Better pre-processing

These improvements will help with the accurate and proficient handling of noisy language data by NLP.

Domain Adaptation and Fine-Tuning

Finally, domain adaptation and fine-tuning are crucial for applying general NLP models to more specific domains and represent an important area for the expansion of NLP technologies.

Applications of NLP

From customer service to healthcare, NLP has numerous applications. It can be used to improve:

  • customer experience by humanizing chat and voice bots

  • managing high volumes of communication

  • sorting and analyzing customer interaction data

  • helping marketers analyze social media posts and customer data to create more effective strategies.

Business Analytics and Customer Feedback

Customer feedback is critical for businesses to gauge customer needs and behavior. NLP techniques allow for the conversion of raw feedback data into insights through natural language generation (NLG) systems. This automated insight saves valuable time and resources that would otherwise be spent on manual review.

Healthcare and Automated Interpretation

In healthcare, NLP is predominantly used for automated interpretation of electronic health records. But the lack of domain-specific data for specialized NLP applications in healthcare represents a significant challenge for the accurate and proficient handling of this type of data.


In this blog, we explored what natural language processing is, its concepts, techniques, challenges and applications. We saw how easy it has become to communicate with machines using human languages or to generate human-like text. Despite the challenges outlined in this blog, it is evident that NLP is here to stay and will keep improving with advancing machine learning algorithms.


What is Natural Language Processing?

Natural Language Processing (NLP) is the capability of computer programs to comprehend spoken or written human language and is a vital component of artificial intelligence.

What are the main components of NLP?

The key components of NLP are natural language understanding (NLU) and natural language generation (NLG). NLU processes human language to extract meaning and NLG generates text in human language.

How does machine learning help in NLP?

Machine learning plays a vital role in NLP as it allows the models to learn language patterns and context thus making human language understanding more efficient.

What are some applications of NLP?

Some of the applications of NLP include customer service chatbots which provide effective and personalized customer service, sentiment analysis which helps businesses analyze customer feedback and make improvements, in healthcare it helps automate the tedious task of interpreting electronic health records. These applications are just but a few examples of how NLP can impact various industries.

How can I get started in NLP?

There are various training courses which cover the basics of NLP and advanced topics like deep learning for NLP. Apart from these courses, participating in challenges and projects as well as contributing to open source projects and keeping tabs with industry blogs and journals would aid in establishing a foothold in the field of NLP.

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