Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that gives machines the ability to process and analyze large amounts of natural language text and voice data. Combining the disciplines of linguistics with machine and deep learning, the goal of NLP is to understand, interpret, and manipulate the human language, including the intent and sentiment—and respond the same way as humans do.
Examples of NLP applications include translating from one language to another, recognizing and responding to voice commands, summarizing large volumes of text data, uncovering data insights from social media channels, and more.
How Does NLP Work?
Two main phases are involved in natural language processing. These are data pre-processing and algorithm development.
In data pre-processing, the human language is separated into fragments, prepared, and cleaned so that the machines can analyze it. The process transforms the text data into structures that algorithms can work with. Several tasks can be performed to achieve this:
- Tokenization breaks down the text into smaller units.
- Part-of-speech tagging marks words based on the respective part of speech.
- Stop-word removal filters and removes words that add little or no unique information.
- Lemmatization and stemming reduce words to their root forms.
Once the data is ready; the next phase is to build and train an NLP algorithm for a specific task. Two main types that are commonly used:
- Rules-based approach uses linguistic rules that are designed by knowledge or linguistics experts.
- Machine learning algorithms leverage statistical methods to learn how to perform tasks based on training data, and to continuously learn as more data is processed. Combined with deep learning and neural networks, these NLP algorithms can make associations between a textual input and output, and by training, can make predictions for new texts.
What are Common Natural Language Processing Tasks?
Human language is complex and ever-evolving. It involves many layers that make it difficult for machines to accurately understand and identify its intended meaning. From sarcasm to metaphors, these irregularities and contextual elements of the human language must be learned by computers for NLP applications to be useful. Some of the tasks that aim to make sense of human text and voice data are:
- Sentiment analysis extracts subjective information from a text such as emotions, confusion, opinions, and more.
- Speech recognition converts voice data into text data. It is the opposite of text-to-speech which transforms units of text into spoken representation.
- Automatic summarization produces a summary of a lengthy text such as research papers, novels, and newspaper articles.
- Natural language generation transforms data into plain human language.
Examples of Real-World Use Cases of NLP
From interacting with chatbots to streamlining business operations, natural language processing is growing in importance across industries. Some of its applications include:
- Analyzing customer feedback from social media reviews and conversations
- Improving customer support with intelligent chatbots that can recognize contextual clues about human requests to offer better responses
- Augmenting on-site search experience with a search engine that can detect the context and nuances behind search queries to deliver more accurate product search results
- Detecting spam by identifying the use of words that indicate fraud or phishing
- Examining medical records to uncover information that identifies, predicts, and ideally prevents diseases
- Detecting plagiarism, grammatical errors, and more