What is NLP?
Natural language processing (NLP) is a machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language. Organizations today have large volumes of voice and text data from various communication channels like emails, text messages, social media newsfeeds, video, audio, and more. They use NLP software to automatically process this data, analyze the intent or sentiment in the message, and respond in real-time to human communication.
Why is NLP important?
Natural language processing is critical to fully analyze text and speech data efficiently. It can work through the differences in dialects, slang, and grammatical irregularities typical in day-to-day conversations. Companies use it for several automated tasks, such as to:
• 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
You can also integrate NLP in customer-facing applications to communicate more effectively with customers. For example, a chatbot analyzes and sorts customer queries, responding automatically to common questions and redirecting complex queries to customer support. This automation helps reduce costs, save agents from spending time on redundant queries, and improves customer satisfaction.
What are NLP use cases for business?
Businesses use 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.
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 Q&A chatbot solution to address student questions using machine learning (ML) technology
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 customer's 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.
How does NLP work?
Natural language processing combines computational linguistics, machine learning, and deep learning models to process human language.
Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Researchers use computational linguistics methods, such as syntactic and semantic analysis, to create frameworks that help machines understand conversational human language. Tools like language translators, text-to-speech synthesizers, and speech recognition software are based on computational linguistics.
Machine learning is a technology that trains a computer with sample data to improve its efficiency. Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to teach NLP applications to recognize and accurately understand these features from the start.
Deep learning is a specific field of machine learning which teaches computers to learn and think like humans. It involves a neural network that consists of data processing nodes to resemble human brain operations. With deep learning, computers recognize, classify, and co-relate complex patterns in the input data.
NLP implementation steps
Typically, the NLP process begins by gathering and preparing unstructured text or speech data from sources like cloud data warehouses, surveys, emails, or internal business process applications.
The NLP software uses pre-processing techniques such as tokenization, stemming, lemmatization, and stop word removal to prepare the data for various applications.
- 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.
Researchers use the pre-processed data to train NLP models with machine learning to perform specific applications based on the provided textual information. Training NLP algorithms requires feeding the software with large data samples to increase their accuracy.
Deployment and Inference
Machine learning 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 it is designed for. You can run the NLP application on live data to and obtain the required output.
What are NLP tasks?
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 turns voice data into text. The process involves breaking words into smaller parts and overcoming challenges like accents, slurs, intonation, and improper 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 software uses natural language processing to convert text or speech from one language to another while retaining contextual accuracy. The AWS service supporting 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 to France for a holiday, and she indulged herself in the local cuisines.
The NLP software will pick 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 is an AI-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.
What are the approaches to natural language processing?
We give some common approaches to natural language processing below.
Supervised NLP methods train the software with a set of labeled or known input and output. The program first processes large volumes of known data and learns how to produce the correct output from any unknown input. For example, companies train NLP tools to categorize documents according to specific labels.
Unsupervised NLP uses a statistical language model to predict the pattern that occurs when it is fed by non-labeled input. For example, the autocomplete feature in text messaging suggests relevant words that make sense for the sentence by monitoring the user's response.
Natural language understanding
Natural language understanding (NLU) is a subset of NLP that focuses on analyzing the meaning behind sentences. NLU allows the software to find similar meanings in different sentences or to process words that have different meanings.
Natural language generation
Natural language generation (NLG) focuses on producing conversational text like humans do based on specific keywords or topics. For example, an intelligent chatbot with NLG capabilities can converse with customers in similar ways that customer support personnel do.
How can AWS help with your NLP tasks?
AWS provides the broadest and most complete set of AI/ML services for customers of all levels of expertise connected to a comprehensive set of data sources.
For customers that lack ML skills, need faster time-to-market, or want to add intelligence to an existing process or an application, AWS offers a range of machine learning-based language services that allow companies to easily add intelligence to their AI applications through pre-trained APIs for speech, transcription, translation, text analysis, and chatbot functionality. Services include Amazon Comprehend to discover insights and relationships in text, Amazon Transcribe for automatic speech recognition, Amazon Translate for fluent translation of text, Amazon Polly for natural sounding from text to speech, Amazon Lex to build chatbots to engage with customers, and Amazon Kendra to do an intelligent search of enterprise systems to quickly find the content one is looking for.
For customers who want to create a standard NLP solution across their business, Amazon SageMaker makes it easy to prepare data and build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows, including no-code offerings for business analysts. With Hugging Face on Amazon SageMaker, you can deploy and fine-tune pre-trained models from Hugging Face, an open-source provider of natural language processing (NLP) models known as Transformers, reducing the time it takes to set up and use these NLP models from weeks to minutes.
Get started with natural language processing (NLP) by creating an AWS account today.