I used Grammarly to help me write this piece. Grammarly used natural language processing to help me make this article look great.
That’s how prevalent natural language processing use cases have become. NLP technologies have trekked a long way, from writing an article and transcribing sales calls to retrieving large amounts of relevant information and truly understanding what the user means.
The evolution of computational linguistics has made it easy for machines to understand human languages, reducing the gaps between human-computer interactions. Natural language processing software enhances customer experience, automates data entries, improves search recommendations, and strengthens security efforts across industries.
Natural language processing (NLP) is an artificial intelligence (AI) technology that allows computer programs to interpret text and spoken words to understand human language better.
NLP uses machine learning (ML) algorithms, rule-based modeling, and deep learning models to help computers process language data to analyze messages' intent and sentiment.
If you’ve used GPS navigation to find your way around a new city or yelled across the room at a voice assistant to switch the lights on – congrats, you’ve met an NLP program!
Thanks to natural language processing, computer applications can respond to spoken commands and summarize large amounts of text in real-time to interact with humans meaningfully and expressively.
NLP is all around us, even if we don’t necessarily notice it. Virtual assistants, customer service chatbots, transformer models, predictive text – all are made possible with NLP technology that understands and filters our requests. The programs bridge computers and humans to organize business operations, revitalizing productivity through finely tuned interactions.
The techniques of NLP training rely on deep learning and algorithms to interpret and make sense of human language.
Deep learning models process unstructured data or qualitative data that cannot be analyzed using conventional tools such as voice and text. They transform it into structured data that can fit into databases we are familiar with to provide usable insights.
Natural language processing extracts contextual information by breaking down language into individual words and identifying their relationships. Doing this allows for a more accurate indexing and segmentation process – one that’s based on sentiment and intent.
Before a model can process any text data, it has to preprocess it into a format the machine can comprehend. There are several data processing techniques available.
Tokenization, the first step for converting raw data into a format the machine can grasp, is dividing the text into smaller units known as tokens. The machine easily understands the text once they’re broken down into words or phrases. Since machines only understand numerical data, the tokenized text is represented as numerical tokens for the programs.
Consider the following text entered by a user:
"There is a bank across the bridge."
Text understood by the machine after tokenization:
["There", "is", "a", "bank", "across", "the", "bridge", "."]
The next preprocessing step in NLP removes common words with little-to-no specific meaning in the text. These words, known as stop words, include articles (the/a/an), “is,” “and,” are,” and so forth. This step eliminates non-useful words and provides a meaningful, efficient, and accurate understanding of the text.
Consider the exact sample text entered by a user:
"There is a bank across the bridge."
Text understood by the machine after removing stop words:
["There", "bank", "across", "bridge", "."]
Stemming and lemmatization refers to the techniques NLP applications use to simplify words and text analysis by reducing them to their base form.
Stemming is a rule-based approach that removes prefixes and suffixes to return the words to their fundamental forms or stems. The process doesn’t require a lot of computational power, and the resulting base words may not always make sense, but they help the program facilitate text analysis.
For example, the word “sharing” will result in a “shar” stem.
A limitation of stemming is that several semantically unrelated words can shareholder share one stem.
Lemmatization is a dictionary-based approach to converting words to their morphological form, aka lemma. The process requires high computational effort due to the need for dictionary lookups. The resulting lemma will always be a valid word contextually and as a part of speech.
For example, the word “sharing” will result in a “share” lemma.
Since our machine friends only get numbers and algorithms, the raw text we enter must be converted into numerical representations. Feature extraction helps retain the relevant information and simultaneously reduces the complexity of the data to capture only the most necessary patterns and relationships.
Different techniques may be used to achieve this outcome based on the NLP task.
NLP algorithms are generally rule-based or trained on machine learning models. Continuous training and feedback loops can create large knowledge reservoirs, better predict human intention, and minimize false responses.
Natural language processing uses AI techniques or tasks to process, comprehend, and generate natural (human) language. They improve human-computer interaction and facilitate effective communication through language-based applications.
You know who hasn’t forgotten their 6th-grade grammar lessons? NLP.
Part of speech (POS) tagging, or grammatical tagging, allows NLP applications to identify individual words in a sentence to determine their meaning in the context of that sentence. This allows computers to tell the difference between nouns, verbs, adjectives, and adverbs and understand their relationships.
As shown in the example below, POS tagging means NLP programs have the power to contextualize the verb “like” in the phrase “I like the beach” and identify “like” as an adverb in the sentence “I am like Mark.”
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The concept isn’t as complicated as it sounds; it just means that NLP programs can identify the intended meaning of the same word when used in different contexts.
Through semantic analysis (i.e., extracting meaning from text and parsing) computers can interpret sentences and relationships between individual words to make the most sense in a particular context.
The word "bark" in the above example has two different meanings.
NLP applications distinguish between a dog’s bark and tree bark through word sense disambiguation.
Natural language processing applications can identify words for specific categories, such as people’s names, places, and names of organizations. Through named-entity recognition (NER), NLP software extracts entities and understands their relationship to the rest of the text.
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In the above example, the NLP task of named entity recognition identifies “Microsoft” and “Bill Gates” as an organization and person, respectively.
High-level NLP tasks such as question answering and information retrieval (more on that later) require computers to identify all words that refer to the same entity. This process, known as co-reference resolution, helps programs determine persons/objects connected to specific pronouns.
Co-reference resolution is also why computers know when an idiomatic expression is part of a text.
NLP programs benefit from understanding the process of converting spoken language into – more or less – computer language. Speech recognition is essential for facilitating natural and intuitive human-computer interactions.
Let’s look at a couple of examples of speech recognition as a part of natural language processing.
NLP programs will always find that important document just when you need it because of their powerful ability to retrieve information from large data sets. The goal of information retrieval as an NLP task is to offer users accurate and useful information from text collection through text mining.
Ever wondered how customer service bots can almost always tell how you’re feeling? It’s all thanks to sentiment analysis – an automated process recognizing emotional tone and expressed sentiments in various use cases.
Machine learning models can be trained on sentiment analysis using sentiment labeling (positive, negative, neutral) classification, post-processing, and sentiment evaluation.
Sentiment analysis is a great way for companies to gain customer insight through product reviews and monitor their brands based on social media sentiments.
The NLP task of automatically translating text or spoken content from one language to another is heavily used in machine translation software. Machine translation aims to provide accurate and coherent translations while maintaining contextual precision.
Translation models also use speech recognition. They’re built to improve global communication and break down language barriers in business, education, healthcare, and international relations.
Ever thought an email was legit and replied to it, but it was just spam? Me, too.
The NLP task of automatically recognizing irrelevant messages from a large messaging group, such as emails and social media posts, and removing them is called spam detection.
The process helps distinguish fraudulent messages from genuine ones and ensures the safety of users on communication platforms.
Programming languages are to NLP what a moth is to a flame. Although many languages and libraries support natural language processing tasks, a few popular ones exist.
The most used programming language for NLP tasks, libraries, and deep learning frameworks is written for Python.
Statisticians widely use the programming language for statistical computing and graphics NLP models written in R. This includes Word2Vec and TidyText.
Natural language processing techniques are used in many business cases to improve operational efficiency, productivity, and mission-critical processes.
The rise of conversational AI has transformed how chatbots and virtual assistants engage with humans, especially in customer service.
NLP fuels the human-like capabilities of chatbots to scale automated customer support while maintaining economical operations. Chat and voice bots can offer personalized recommendations and localized chat functionalities to aid in the buying process, answer FAQs, and assist users in real-time.
Speech-to-text features are also beneficial in tracking call center analytics to transcribe voice data into text.
Social media monitoring
Sentiment analysis on social platforms helps evaluate customer feedback and reviews to understand consumer satisfaction through valuable data insights.
Social media monitoring tools are powered by natural language processing to grant listening, tracking, and content collection functionalities. These applications see wide use in performing market research, tracking trend analysis, and identifying patterns across different social networks.
Insights extraction and fraud detection
The healthcare and legal industries use NLP technology to extract high-quality, relevant data insights from large volumes of clinical trial data, scientific literature, and legal contracts.
As with spam detection, NLP technology can detect fraudulent activities by perceiving patterns in data. This is especially useful in the financial sector for monitoring transactions.
While there’s only one differentiating term in natural language processing, natural language understanding, and natural language generation, a few differences exist among the three concepts.
NLP is a branch of AI that helps computers understand, interpret, and generate human language. Common NLP tasks include speech recognition, sentiment analysis, and named entity recognition.
NLP is widely used in voice assisting for summarizing large amounts of text and translation services.
A subset of NLP, NLU software focuses on the comprehension of the text to extract meaning from the data. It combines software logic, linguistics, ML, and AI to make sense of natural language.
Common NLU tasks include:
On the other end of NLU is NLG technology, the branch of AI that generates written or spoken text from a dataset. It lets computers give provide feedback to humans in a language that is understandable to us, not machines.
Common NLG tasks include:
While NLP might seem like a sorcerer, it isn’t. It combines various powerful computational abilities making it useful in many tasks that make human tasks more efficient.
Whether it’s through chatbot greetings or text summarization, the world of NLP continues to strive to provide valuable insights from large human language datasets. NLP technologies are making our personal and professional lives more engaging, personalized, and interactive while we navigate our new data-centric world.
One of the most popular NLP functionalities is its usage in voice assistants. Learn more about how voice recognition works and the features it offers that enable you to yell commands at it.
This article was originally published in 2019. It has been updated according to new editorial guidelines, with new resources and recent examples.
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