Overview
This lecture introduces Natural Language Processing (NLP), its real-world applications, core techniques, and basic steps for processing human language with computers.
What is Natural Language Processing (NLP)?
- NLP is a field of Artificial Intelligence that enables machines to read, understand, and derive meaning from human languages.
- NLP combines linguistics and computer science to help computers interpret and process text and speech.
Real-World Applications of NLP
- Virtual assistants, website chatbots, and automated customer service use NLP to interact with humans.
- Everyday tools like autocorrect and plagiarism checkers rely on NLP to process and analyze language.
- NLP automates responses and saves manpower by mimicking human conversational behavior.
Basic NLP Techniques and Steps
- Segmentation breaks documents into sentences using punctuation.
- Tokenization splits sentences into individual words, each called a token.
- Stop words (e.g., 'are', 'the') are filtered out as they add little meaning.
- Stemming reduces words (e.g., 'skipping', 'skipped') to their root form.
- Lemmatization finds the base form (lemma) of words, accounting for tense, gender, etc.
- Part-of-speech tagging labels words as nouns, verbs, etc., to inform grammar understanding.
- Named Entity Tagging flags names of people, places, movies, and other important entities.
- Machine learning algorithms like Naive Bayes help teach the model to interpret sentiment and context.
Common NLP Exam Question
- Example: Which NLP technique separates words from sentences?
a) Stemming, b) Tokenization, c) Lemmatization, d) Segmentation
Key Terms & Definitions
- NLP (Natural Language Processing) — AI branch that processes and understands human language.
- Tokenization — Breaking text into individual words or tokens.
- Stop Words — Common words with little meaning, often removed in preprocessing.
- Stemming — Cutting words down to their root form by removing suffixes or prefixes.
- Lemmatization — Reducing words to their base or dictionary form (lemma).
- Part-of-Speech Tagging — Assigning labels (noun, verb, etc.) to each word.
- Named Entity Tagging — Identifying proper nouns like names, places, and brands.
Action Items / Next Steps
- Review NLP techniques and their definitions.
- Answer the example exam question about tokenization.
- Explore further learning resources or courses for in-depth NLP study.