Overview
This lecture introduces Natural Language Processing (NLP), a field of artificial intelligence focused on enabling computers to understand and generate human language, covering its main tasks, subtasks, and micro-tasks.
Introduction to Natural Language Processing (NLP)
- NLP is a field within AI that enables computers to understand, interpret, and generate human language.
- Unlike basic speech-to-text, NLP involves understanding meaning and emotional tone in spoken or written language.
Main Tasks in NLP
- Natural Language Understanding (NLU): Comprehending user intent and meaning, e.g., voice assistants detecting homophones.
- Natural Language Translation: Automatically converting text or speech between languages.
- Natural Language Generation (NLG): Producing human-like language from data or intent, seen in chatbots and automated product descriptions.
Subtasks in NLP
- Summarization: Creating concise summaries of long or complex documents.
- Question Answering: Responding to user questions by extracting or computing answers.
- Information Extraction: Identifying structured data (like entities or events) from unstructured text.
- Sentiment Analysis: Detecting the emotional tone or polarity of text.
- Semantic Analysis: Determining the meaning conveyed by text.
- Auto-Categorization: Classifying documents into categories for further processing.
Information Extraction Details
- Information extraction converts large amounts of unstructured text (like contracts or logs) into structured, searchable databases.
- This involves identifying entities (people, companies), events, and relationships within documents.
Micro-Tasks in NLP
- Part-of-Speech Tagging: Labeling each word with its grammatical role.
- Parsing: Analyzing sentence structure as a tree for understanding relationships.
- Sentence Boundary Detection: Identifying where sentences begin and end.
- Word Boundary Detection: Finding word divisions, especially challenging in languages without spaces (e.g., Chinese).
- Named Entity Recognition: Detecting specific entities such as names, places, or products in text.
- Topic Segmentation: Dividing documents into topics or sections.
- Automatic Summarization: Producing brief overviews of longer documents.
- Discourse Analysis: Understanding conversational flow and context.
Applications and Implications
- NLP automates tasks involving reading/listening, searching for information, and composing responses.
- Professions ripe for NLP automation include customer support, law, and counseling.
Key Terms & Definitions
- Natural Language Processing (NLP) — AI field dealing with interaction between computers and human languages.
- Natural Language Understanding (NLU) — Task of comprehending the meaning of language.
- Natural Language Generation (NLG) — Automated creation of human-like language output.
- Information Extraction — Pulling structured data from unstructured text.
- Sentiment Analysis — Identifying the emotional tone of text.
- Named Entity Recognition — Detecting specific names or key domain entities in text.
Action Items / Next Steps
- Review the distinctions between NLP main tasks, subtasks, and micro-tasks.
- Prepare for deeper discussions on information extraction, sentiment analysis, and semantic analysis in upcoming lectures.