By the end of this unit you should:
Read about information extraction fundamentals and applications.
Information Extraction (IE) transforms unstructured text into structured data by identifying and extracting specific types of information. Unlike text classification which assigns labels to entire documents, IE locates and extracts particular pieces of information within documents.
Key IE tasks include:
Watch this comprehensive introduction to NER techniques and evaluation.
This video (6 minutes) covers NER algorithms, tag schemes (BIO, BILOU), evaluation metrics, and common challenges like entity ambiguity and domain adaptation.
Experiment with NER across different languages and entity types.
Use the interactive tool to test NER performance across languages and customize entity recognition for specific domains.
Build systems to extract relationships between entities.
Learn to identify and extract semantic relationships between entities using pattern-based and machine learning approaches.
Define patterns to extract relationships:
No patterns added yet
Watch how to extract events and build interactive timelines.
This video (6 minutes) demonstrates event detection, temporal ordering, and timeline visualization for news analysis and historical research.
Apply IE to real-world document processing tasks.
Extract structured information from different document types including resumes, invoices, and forms using IE techniques.
Build interactive knowledge graphs from extracted information.
Combine entity and relation extraction to create comprehensive knowledge graphs that can be queried and visualized.
Test your understanding of information extraction:
1. What is the main goal of Named Entity Recognition?
2. Relation extraction focuses on:
3. Knowledge graphs represent: