Natural Language Processing (NLP): The Master Guide 2024

NLP is a machine learning technology that allows computers to comprehend and interact with human language. Modern organizations generate vast amounts of speech and text data from various sources, such as emails, social media, video, and audio. NLP software automates the processing of this data, analyzes the intent behind messages, and delivers real-time responses in human conversations.

Why does NLP matter?

Natural language processing (NLP) is crucial for the efficient analysis of text and speech data, overcoming dialects, slang, and grammatical inconsistencies in everyday conversations.

Companies use NLP for automated tasks such as processing, analyzing, and archiving extensive records. It helps analyze consumer feedback or call center records, automate customer service with chatbots, answer who-what-when-where questions, and classify and extract text.

Integrating NLP into customer-facing applications enhances communication with customers. For example, a chatbot can analyze and sort client queries, handle common questions, and escalate complex issues to human agents. This automation reduces costs, saves agents’ time on repetitive queries, and improves customer satisfaction.

What are commercial NLP use cases?

Natural language processing (NLP) software assists businesses in automating, streamlining, and simplifying tasks with precision. Here are some examples of its applications:

Redacting sensitive data

Insurance, legal, and healthcare companies manage vast quantities of sensitive documents, including medical records, financial information, and private data. To protect this information, companies utilize NLP technology to redact personally identifiable information and safeguard sensitive data, rather than relying on manual review. For instance, insurance firms can leverage Chisel AI and Amazon Comprehend to extract policy numbers, expiration dates, and other customer details from unstructured documents efficiently.

Engagement of customers

NLP technology enhances the human-like interaction capabilities of chat and voice bots with clients. Chatbots help businesses boost customer service while lowering operational costs. For example, PubNub, a chatbot software developer, uses Amazon Comprehend to deliver localized chat features to a global audience. T-Mobile employs NLP to identify terms in customers’ text messages and offer personalized recommendations. Additionally, Oklahoma State University utilizes machine learning to develop a Q&A chatbot that addresses student inquiries.

Business analytics

Marketers use NLP tools such as Amazon Comprehend and Amazon Lex to gauge customer sentiment towards products or services. These tools scan for specific terms to evaluate consumers’ moods and emotions in written feedback. Success KPI provides natural language processing solutions for targeted sentiment analysis and actionable insights derived from call analytics.

NLP: How Does It Work?

Natural language processing (NLP) leverages computational linguistics, machine learning, and deep learning techniques to analyze and interpret human language.

Computational Linguistics

Computational linguistics involves using computers and software to model human language. Researchers apply methodologies such as syntactic and semantic analysis to create frameworks that enable robots to understand human discourse. This field supports tools like language translators, text-to-speech synthesizers, and speech recognition software.

Machine Learning

Machine learning enhances computer efficiency by training systems on example data. Human language encompasses sarcasm, metaphors, variations in phrase structure, and exceptions in grammar and usage, which take years to master. Programmers use machine learning to teach NLP programs to recognize and interpret these nuances from the outset.

Deep Learning

Deep learning, a subset of machine learning, trains computers to learn and think similarly to humans. It utilizes neural networks with data processing nodes that mimic the human brain. Deep learning enables computers to identify, classify, and correlate complex patterns in input data.

Steps for implementing NLP

To implement NLP, follow these steps:

Data Collection and Preparation

Begin by gathering and preparing unstructured text or audio data from sources such as cloud data warehouses, surveys, emails, or internal business process applications.

Pre-processing

NLP software uses various pre-processing techniques to prepare data for different applications:

• Tokenization: Splits a statement into individual words or phrases.
• Stemming and Lemmatization: Reduce words to their root forms. For example, both “starting” and “starts” are reduced to “start.”
• Stop Word Removal: Eliminates common words like “for” and “with” to enhance the meaningfulness of the text.

Training

Train NLP models using pre-processed data and machine learning techniques to perform specific tasks. To improve accuracy, the model needs to be trained on large data samples.

Inference and Deployment

Deploy or integrate the trained model into an existing production environment. The NLP model will predict outputs based on input data for the intended application. Run the NLP program on live data to achieve the desired results.

What Are NLP Tasks?

NLP approaches decompose human text or speech into manageable components that computer programs can easily understand. Common NLP text processing and analysis tasks include:

• Part-of-Speech Tagging: This technique involves categorizing words in a sentence based on their context, such as nouns, verbs, adjectives, and adverbs. It helps the computer understand how words relate to each other to form meaningful sentences.
• Word Sense Disambiguation: Words can have different meanings depending on the context. For example, “bat” can refer to a nocturnal animal or a baseball equipment. NLP software uses language models or dictionary definitions to determine the intended meaning of a word in a given context.
• Speech Recognition: This task converts spoken language into text. It involves breaking down spoken words and interpreting variations such as accents, slurs, intonation, and nonstandard grammar usage. Speech recognition is crucial for applications like transcription, often performed using services such as Amazon Transcribe.

NLP Tasks

• Machine Translation: Machine translation software translates text or speech from one language to another while preserving contextual accuracy. Services like Amazon Translate enable effective machine translation using natural language processing.
• Named Entity Recognition: This task involves identifying unique names for individuals, locations, events, companies, and other entities within a text. NLP software uses named-entity recognition to establish relationships between these entities. For example, in the sentence, “Jane went on a vacation to France, and she indulged in the local cuisines,” the NLP engine identifies “Jane” and “France” as special entities. Co-reference resolution further clarifies that “Jane” and “she” refer to the same person.
• Sentiment Analysis: Sentiment analysis interprets emotions within textual data using artificial intelligence. NLP software detects underlying sentiments such as discontent, happiness, doubt, and regret, providing insights into the emotional tone of the text.

Methods of Natural Language Processing

• Supervised NLP: This approach involves training software with labeled input-output pairs. The system learns from vast amounts of pre-labeled data to generate accurate outputs for new, unlabeled inputs. Companies often use supervised NLP to train technologies for tasks like document classification, where documents are categorized based on predefined labels.
• Unsupervised NLP: In unsupervised NLP, the software identifies patterns and structures from unlabeled data using statistical language models. For instance, in text messaging applications, the autocomplete feature uses unsupervised techniques to suggest relevant phrases based on the user’s input, even without explicit labels.

Natural Language Processing Methods

• Natural Language Understanding (NLU): A subset of NLP, NLU focuses on analyzing and interpreting the meaning of sentences. It enables software to discern similar meanings in different sentences and to understand words with multiple meanings, improving the comprehension of nuanced language.
• Natural Language Generation (NLG): NLG involves creating human-like text from structured data or keywords. For example, an intelligent chatbot with NLG capabilities can generate conversational responses, simulating interactions with customer service representatives.

How AWS Assists with NLP Tasks

AWS offers a broad spectrum of AI/ML services designed for various skill levels, allowing easy integration of intelligence into applications and processes. These services are connected to multiple data sources and cater to diverse needs, from quick deployment to advanced ML capabilities:

• Amazon Comprehend: Provides text analysis and insights, revealing relationships and themes within text.
• Amazon Transcribe: Automates the conversion of speech to text, facilitating transcription services.
• Amazon Translate: Delivers accurate and efficient text translation across multiple languages.
• Amazon Polly: Converts text into lifelike speech, enhancing user experiences with natural-sounding audio.
• Amazon Lex: Powers chatbots and conversational interfaces for improved customer engagement.
• Amazon Kendra: Utilizes intelligent search to swiftly locate and retrieve relevant business information.

For standardized NLP solutions, Amazon SageMaker offers streamlined data preparation, ML model development, and deployment with managed infrastructure. It includes no-code options for business analysts and supports integration with Hugging Face on SageMaker, allowing rapid deployment and customization of pre-trained NLP models, known as Transformers, from an open-source vendor. This reduces setup time from weeks to minutes.