Linguistic Analysis and Information Extraction

Why this module belongs in the course

Linguistic tools predict structure from context. Their labels are useful evidence, not unquestionable facts, especially across domains and languages.

Before you begin

The concepts and project evidence from Module 9. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Part-of-Speech Tagging and Dependency Parsing65 min · Intermediate
2. Lesson 2Named Entity Recognition with Span-Level Evaluation70 min · Intermediate
3. Lesson 3Rule-Based Information Extraction and Relation Patterns65 min · Intermediate
4. Lesson 4Project: Build an Auditable Entity and Relation Extractor100 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• https://spacy.io/usage/linguistic-features
• https://universaldependencies.org/u/dep/
• https://spacy.io/usage/rule-based-matching
• https://huggingface.co/docs/hub/datasets-cards
• https://docs.python.org/3/library/re.html