Natural Language Processing · free preview

Text Normalization and Regular Expressions

The normalization pipeline, the regular expression operators, capture groups and substitutions, and sentence segmentation pitfalls.

1. Text Normalization and Regular Expressions

2. The regular expression operators

The regular expression operators

3. Tokenizing and rewriting with Python's re module

Tokenizing and rewriting with Python's re module

4. Reading a pattern

Which of these strings matches the regular expression D[a-z]{4}\d{2} (anchors omitted, so a match can occur anywhere)?

5. Counting matches

How many substrings of this log line match \d{3}-\d{4}?

"99-5981; 183-6326; 823-7318; 8789-6672; 256-9285."

6. Rewriting with capture groups

The substitution re.sub(r"(\w+) (\d+) (\w+)", r"\3-\1", s) is applied to s = "bay 09 south". What is the resulting string? (Type it exactly, lowercase.)

7. Word boundaries

Which of these strings contains a match for the pattern \bwake\b?

8. Counting the language of a pattern

How many strings of length 7 over the alphabet {0,1} end with 10? (That is, strings matching (0|1)*10 with total length exactly 7.)

Practice this interactively

Free account · instant grading · spaced review that schedules itself.

Start this course for free