What is Natural Language Processing (NLP)?

Companies use natural language processing for several automated tasks, such as:

• Process, analyze, and archive large documents.
• Analyze customer feedback or call center recordings
• Run chatbots for automated customer service
• Answer who-what-when-where questions
• Classify and extract text

Businesses use natural language processing (NLP) software and tools to simplify, automate, and streamline operations efficiently and accurately. We give some example use cases below.

Sensitive data redaction

Businesses in the insurance, legal, and healthcare sectors process, sort, and retrieve large volumes of sensitive documents like medical records, financial data, and private data. Instead of reviewing manually, companies use NLP technology to redact personally identifiable information and protect sensitive data. For example, Chisel AI helps insurance carriers extract policy numbers, expiration dates, and other personal customer attributes from unstructured documents with Amazon Comprehend.

Customer engagement

NLP technologies allow chat and voice bots to be more human-like when conversing with customers. Businesses use chatbots to scale customer service capability and quality while keeping operational costs to a minimum. PubNub, which builds chatbot software, uses Amazon Comprehend to introduce localized chat functionality for its global customers. T-Mobile uses NLP to identify specific keywords in customers' text messages and offer personalized recommendations. Oklahoma State University deploys a Q&A chatbot solution to address student questions using machine learning technology.

Business analytics

Marketers use NLP tools like Amazon Comprehend and Amazon Lex to gain an educated perception of what customers feel toward a company's product or services. By scanning for specific phrases, they can gauge the customers' moods and emotions in written feedback. For example, Success KPI provides natural language processing solutions that help businesses focus on targeted areas in sentiment analysis and help contact centers derive actionable insights from call analytics.

Natural language processing (NLP) techniques, or NLP tasks, break down human text or speech into smaller parts that computer programs can easily understand. Common text processing and analyzing capabilities in NLP are given below.

Part-of-speech tagging

This is a process where NLP software tags individual words in a sentence according to contextual usages, such as nouns, verbs, adjectives, or adverbs. It helps the computer understand how words form meaningful relationships with each other.

Word-sense disambiguation

Some words may hold different meanings when used in different scenarios. For example, the word "bat" means different things in these sentences:

• A bat is a nocturnal creature.
• Baseball players use a bat to hit the ball.

With word sense disambiguation, NLP software identifies a word's intended meaning, either by training its language model or referring to dictionary definitions.

Speech recognition

Speech recognition turns voice data into text. The process involves breaking words into smaller parts and understanding accents, slurs, intonation, and nonstandard grammar usage in everyday conversation. A key application of speech recognition is transcription, which can be done using speech-to-text services like Amazon Transcribe.

Machine translation

Machine translation software uses natural language processing to convert text or speech from one language to another while retaining contextual accuracy. The AWS service that supports machine translation is Amazon Translate.

Named-entity recognition

This process identifies unique names for people, places, events, companies, and more. NLP software uses named-entity recognition to determine the relationship between different entities in a sentence.

Consider the following example: "Jane went on a vacation to France, and she indulged herself in the local cuisine."

The NLP software identifies "Jane" and "France" as the special entities in the sentence. This can be further expanded by co-reference resolution, determining if different words are used to describe the same entity. In the above example, both "Jane" and "she" pointed to the same person.

Sentiment analysis

Sentiment analysis is an artificial intelligence-based approach to interpreting the emotion conveyed by textual data. NLP software analyzes the text for words or phrases that show dissatisfaction, happiness, doubt, regret, and other hidden emotions.

Typically, NLP implementation begins by gathering and preparing unstructured text or speech data from sources like cloud data warehouses, surveys, emails, or internal business process applications.

Pre-processing

The NLP software uses pre-processing techniques such as tokenization, stemming, lemmatization, and stop word removal to prepare the data for various applications.

Here's a description of these techniques:

• Tokenization breaks a sentence into individual units of words or phrases.
• Stemming and lemmatization simplify words into their root form. For example, these processes turn "starting" into "start."
• Stop word removal ensures that words that do not add significant meaning to a sentence, such as "for" and "with," are removed.

Training

Researchers use the pre-processed data and machine learning to train NLP models to perform specific applications based on the provided textual information. Training NLP algorithms requires feeding the software with large data samples to increase the algorithms' accuracy.

Deployment and inference

AI experts then deploy the model or integrate it into an existing production environment. The NLP model receives input and predicts an output for the specific use case the model is designed for. You can run the NLP application on live data and obtain the required output.