Natural language processing (NLP) is a field of artificial intelligence that seeks to break down human language into components that a computer can comprehend. Smart speakers, chatbots, translation software, and voice-activated devices use NLP algorithms to translate what you type or say into data or commands that computer programs can use.
There are limits to what NLP can do. The applications may have trouble with colloquial language, idioms, sarcasm, and other common communication elements that humans can grasp immediately. However, natural language processing tools are extremely useful in specific settings, and they can streamline work or perform routine tasks.
Here is a closer look at the ins and outs of natural language processing.
How Does Natural Language Processing Work?
Natural language processing takes a step-by-step approach to breaking down speech or text into digital data.
Here are the different elements of the process:
- Tokenization: The first step in the process is to break the speech or text into usable sections, known as tokens. Tokens do not have to be one word. Words that commonly go together, like “higher education,” customer service,” or “one of a kind” can be a single token.
- “Stop word” removal: The next step is to remove words that do not add useful information. These “stop words” can vary depending on the context, but they can often include politenesses like “hello” and “thank you,” prepositions, and some commonly-used adverbs like “actually.” The removal allows the algorithm to focus on actual meaning without wasting processing speed on unnecessary terms.
- Lemmatization and stemming: A lemma is a root word. You can think of it as the word you look up in the dictionary. “Is,” for example, is listed under the lemma “to be.” Lemmatization changes the words into their root forms, which the program can process more easily. This procedure also applies to plural words and other parts of speech. Stemming is a related system that changes words into their root form. For example, “eating,” “eaten,” and “eats,” would all become “eat” in the NLP program.
- Part-of-speech tagging: The final preparation step in the NLP process is to identify the grammatical components of the text. For example, words could be tagged as subjects, objects, verbs, and adjectives. Also, programs can seek out and denote dependent words and clauses to help with context.
There are two common approaches to categorizing and translating language for computer use.
- Rules-based system: Some programs have a collection of rules and assess each word, sentence, or phrase based on these directions. For example, a rule might tell you to use lemmatization instead of stemming or provide specific criteria for choosing stop words. The performance of a system like this depends on the quality of rules, but it is usually effective for providing translations or analyzing basic queries.
- Machine learning-based system: AI makes it possible for NLP programs to learn vocabulary, meanings, and context by analyzing datasets. For instance, a machine-learning NLP program may look at data and learn the uses for various idioms or recognize dialect vocabulary based on the user’s location. However, there is no guarantee that this program will follow a set of rules. This can make it challenging to use when you want reliable results.
Some NLP applications seek to use machine learning to enhance existing rules-based systems.
How New is Natural Language Processing?
The history of natural language processing started after World War II because of a desire to automatically translate between two languages. However, early efforts were mostly unsuccessful until the late 1980s, when scientists invented statistical models that added machine learning to the equation.
NLP wasn’t widely available to consumers until much later. The first basic text-based chatbot, SmarterChild, came to messaging apps in 2001. Apple introduced its popular SIRI virtual assistant in 2011, starting a trend that Google and Microsoft followed.
Though NLP is not a new field, it has only been accurate enough for consumer and business tasks for the past decade.
Here are some of the jobs NLP programs can handle:
- Customer service: Automated customer service bots using NLP are extremely common. 70% of customer interactions involve emerging tech tools like chatbots and automated response systems.
- Translations: Google translate and similar automated programs rely on NLP technology.
- Voice-activated tasks: Home devices and smartphone apps pair voice recognition with NLP. They can respond to commands and interact with users.
- Grammar corrections: Automated text correction programs use NLP to find errors and offer suggestions to writers.
- Market research: NLP plays a role in systems that automatically gauge customer sentiment through online reviews and surveys.
- Predictive text: Smartphones, computers, and search engines predict what you are typing based on analysis from NLP programs.
As natural language processing becomes more sophisticated, applications can handle new tasks, such as data collection from text on the internet.
Benefits of Natural Language Processing
Natural language processing can bring a wide range of benefits to individuals and businesses:
- Streamline services: Companies can use NLP tools like chatbots to manage customer service communications. According to IBM, chatbots can handle 80% of all customer queries. Companies can automate these interactions and save money by hiring fewer customer service representatives or contractors.
- Empower employees: Employees might be concerned that AI-powered tools will take customer service jobs. However, these applications can also give employees enhanced feedback. For example, real-time speech analytics powered by NLP algorithms can provide insights to help the representative communicate better with the customer.
- Ensure quality: Companies can use NLP tools to improve quality across their business. They can use programs to automate data collection from surveys, focus groups, or online reviews. NLP-powered quality assurance software can assess call quality for sales or customer service staff.
- Save time: Employees and individuals can save time by triggering basic tasks via voice controls on their phones or smart home devices.
- Access services: Translation, text editing, and other similar services are expensive. However, automated programs can perform these tasks with a reasonable level of quality.
Cons of Natural Language Processing
There are some drawbacks to natural language processing tools:
- Unintentional bias: NLP programs may be unintentionally biased toward some users in both business and personal settings. The program may not understand certain dialects or colloquial speech, so it may not work well for entire groups of people. For example, a program may not understand non-native speakers of a language or people with an accent from a specific region of the country.
- Amplifying mistakes: NLP-powered AI programs need to be perfect. Poor AI performance can frustrate customers or cause a company to lose subscriptions or sales. Since AI performs the same way for every interaction, a glitch can drive away multiple customers.
It’s important to understand the limitations of NLP tools. In certain situations, such as predictable customer service interactions or text-to-text translations, it can work well. However, asking a program to decipher idiom-filled speech might leave you disappointed. This is why it is essential to get tested and well-developed NLP tools for customer service.
Natural Language Processing Tools
Natural language processing shows up in a variety of settings.
Here is a summary of where you are likely to encounter it today:
- Customer service chatbots;
- Online translation programs;
- AI grammar and writing editors;
- Autofill and autocorrect tools;
- Customer service QA systems;
- Real-time communication analytics;
- Smart speakers;
- Smart home and smart entertainment controls;
- Tools for measuring customer or PR sentiment.
The Future of Natural Language Processing
What is on the horizon for NLP software? Demand is growing rapidly. In 2019, the NLP market was worth $10.34 billion. It is expected to reach $48.46 billion by 2027 with a compound annual growth rate (CAGR) of 21.3%.
Text analytics will likely remain the most popular use for NLP technology, with companies using it to collect and organize data and information from online sources. With the advances in data science, this task is valuable for companies.
Advances could also come in specialized areas, such as fake news detection and machine learning, which could make applications better at understanding human speech.