What is NLP? Natural Language Processing Explained
By - Blink AI Team / First Created on - July 7, 2024
Natural language processing (NLP) is a field of machine learning that empowers computers to interpret, manipulate, and understand human language. In today's data-rich world, organizations have vast amounts of voice and text data from various communication channels such as emails, text messages, social media newsfeeds, videos, and audio recordings. NLP software helps these organizations automatically process this data, analyze the intent or sentiment within messages, and respond in real time to human communication.
Why is NLP Important?
NLP is crucial for efficiently analyzing text and speech data, handling the complexities of dialects, slang, and grammatical inconsistencies found in everyday conversations. Companies leverage NLP for several automated tasks, including:
- Processing and archiving large documents: Automating the analysis and storage of extensive documentation.
- Customer feedback analysis: Understanding sentiment and intent in feedback or call center recordings.
- Running chatbots: Automating customer service interactions.
- Answering queries: Providing responses to common questions.
- Classifying and extracting text: Organizing and pulling specific information from texts.
By integrating NLP into customer-facing applications, businesses can enhance communication efficiency. For instance, a chatbot can handle routine queries automatically and escalate complex issues to human agents, thereby reducing costs and improving customer satisfaction.
NLP Use Cases for Business
Businesses employ NLP to simplify, automate, and streamline their operations accurately and efficiently. Here are some notable use cases:
- Sensitive Data Redaction: In industries like insurance, legal, and healthcare, NLP helps redact personally identifiable information from documents. For example, Chisel AI assists insurance carriers in extracting policy numbers and other personal details using Amazon Comprehend.
- Customer Engagement: NLP technologies enable chat and voice bots to interact more human-like with customers. Businesses use chatbots to scale customer service while minimizing costs. For instance, PubNub uses Amazon Comprehend for localized chat functionality, T-Mobile identifies keywords in text messages for personalized recommendations, and Oklahoma State University uses a Q&A chatbot for student inquiries.
- Business Analytics: Marketers utilize NLP tools like Amazon Comprehend and Amazon Lex to understand customer sentiment towards products or services. Success KPI provides solutions that focus on sentiment analysis, helping contact centers derive actionable insights from call analytics.
How Does NLP Work?
NLP combines computational linguistics, machine learning, and deep learning models to process human language. Here’s a breakdown:
Computational Linguistics: This involves creating frameworks to help machines understand human language through syntactic and semantic analysis. It underpins tools like language translators and speech recognition software.
Machine Learning: This involves training a computer with sample data to improve its efficiency. NLP applications are taught to recognize and understand complex language features such as sarcasm, metaphors, and grammar exceptions.
Deep Learning: This specific field of machine learning uses neural networks to mimic human thinking, recognizing complex patterns in data to improve NLP applications.
NLP Implementation Steps
- Data Gathering and Preparation: Collect unstructured text or speech data from various sources.
- Pre-processing: Prepare the data using techniques like:
- Tokenization: Breaking sentences into individual words or phrases.
- Stemming and Lemmatization: Reducing words to their root forms (e.g., "starting" to "start").
- Stop Word Removal: Eliminating common words that don't add significant meaning (e.g., "for", "with").
- Training: Using pre-processed data to train NLP models with machine learning, improving their accuracy over time.
- Deployment and Inference: Integrate the trained model into a production environment to process live data and generate the required outputs.
Common NLP Tasks
NLP tasks involve breaking down human text or speech into smaller, understandable parts for computer programs:
- Part-of-Speech Tagging: Tagging words in a sentence based on their context (nouns, verbs, etc.).
- Word-Sense Disambiguation: Identifying the correct meaning of a word based on context.
- Speech Recognition: Converting voice data into text, handling accents, intonation, and grammar irregularities.
- Machine Translation: Translating text or speech from one language to another while maintaining contextual accuracy.
- Named-Entity Recognition: Identifying and categorizing unique names (people, places, etc.) in text.
- Sentiment Analysis: Interpreting the emotional tone behind textual data.
Approaches to NLP
Supervised NLP: Training models with labeled data to produce correct outputs for new inputs.
Unsupervised NLP: Using statistical models to predict patterns in non-labeled input, such as suggesting relevant words in autocomplete features.
Natural Language Understanding (NLU): Analyzing the meaning behind sentences to find similar meanings and process words with different interpretations.
Natural Language Generation (NLG): Producing human-like conversational text based on keywords or topics, enhancing chatbot interactions to resemble human support.