Natural language processing (or NLP) utilizes machine-learning methods to equip computers with the ability to converse with users. In other words, NLP enables your computer to understand and use your own language. This form of processing exists at the intersection of computational linguistics and artificial intelligence.

Natural language processing draws on several different computer science resources to communicate with users. NLP applications utilize algorithms, API libraries, and open-source software to make sense of human language.

Many programming languages now come with built-in NLP resources themselves. For instance, Python includes a Natural Language Toolkit (NLTK). This enables programmers to more easily code for NLP-related tasks their software might have to perform.

There are multiple ways for a computer to achieve NLP optimization. Consider these three:

• Hand programming: In the early days of computing, programmers had to hardcode almost everything for their interfaces. To this day, some people might choose to write their own computer programs featuring unique NLP models built from the ground up.
• Machine learning: Computer systems have become more self-sustaining due to the advent of machine-learning algorithms. These programs use statistical methods to reduce dependency on programmers, allowing computers to instead teach themselves through repeated user inputs.
• Neural network utilization: As automation becomes more and more ubiquitous, NLP algorithms will become second nature to most machines. By drawing on the resources of Big Data and utilizing deep-learning models, computers can link up through neural networks to create better NLP algorithms themselves.

Natural language processing has many real-world applications. Here are prominent use cases you can consider:

1. Analyzing grammar: NLP enables your computer to utilize programmed language models to better understand the grammar of datasets. For example, it can recognize which words are verbs, nouns, or adjectives. Lemmatization, part-of-speech tagging, stemming, and word sense disambiguation are some of the other key grammatical tasks NLP enables your computer to perform.

2. Communicating with users: If you’ve ever interacted with a chatbot or used your smartphone’s voice recognition function, you’ve interacted with NLP software. As natural language generation programs become more sophisticated, so do their capability for complex question answering. This is part of the reason why artificial intelligence now feels almost sentient in some cases.

3. Detecting spam: Through semantic analysis, NLP programs can notice which emails you receive are legitimate as well as which are spam. Spam emails often include improper grammar and bizarre syntax, so NLP software goes to work classifying and indexing them away from your main inbox.

4. Extracting information: Natural language processing is very useful for data science purposes because of its ability to comb through large corpora of unstructured data and extract key information. For instance, suppose you’re a health care worker who wants to call up all the literature you can about a specific ailment. NLP’s topic modeling and search capabilities enable you to do so.

5. Recognizing data: Just as NLP can output information you request, it can also recognize inputs you provide it. Natural language understanding (NLU) even enables your computer to break down your vocal or textual commands via a process of segmentation. Named entity recognition allows you to command your phone to perform certain tasks for you.

6. Summarizing text: Some NLP programs can comb through vast amounts of language data and then summarize the content in a much more digestible form for users. This sort of unstructured text summarization is still in its early stages, but it’s likely NLP will soon lead to machines parsing through data faster and relaying key information about that data even more accurately.

7. Translating text-to-speech: On a very pragmatic level, NLP enables your phone to read your text messages to you aloud. It also enables predictive text messaging, relying on both your own vernacular and wider linguistic conventions. Search engines use these same general principles to provide you with options you might want to search for as you begin typing.

8. Understanding voice commands: Speech recognition is one of the key benefits NLP unlocked for computers. As AI becomes more robust, it’s beginning to perform sentiment analysis (not just hearing what you say but how you say it). Speech-to-text relies on natural language generation from both the user and the computer interface in order for both to communicate.