CourseNatural Language Processing: The Complete Course · Module 2: Tokenisation and Word Normalisation · part 16 of 73
Part 16 · Module 2: Tokenisation and Word Normalisation

Lesson 3: Stop words, stemming and lemmatisation, measured

42 min read·14 Oct 2026
Module 2 · Lesson 3 · About 40 minutes · Needs: basic Python; Lesson 1 of this module helps (what a token is) but is not required

The situation

It is Monday 14 September 2026, project week 2. Ines types "charging" into the inbox search, looking for Volt 2 batteries that will not charge. It finds nothing: the messages say "charge" and "charged", and the search matches only the exact word.

Rosa asks whether the computer can treat "charging", "charged" and "charge" as one word. It can, in two ways, and there is an older habit of throwing away little words such as "the" and "not". This lesson measures all three on the Tidewell inbox (simulated data): where each helps, and where it quietly does damage.

INES SEARCHES FOR "CHARGING" exact word 0 messages stem or lemma 121 messages 121 English messages say charge or charged; none says charging
Ines's search for "charging" finds nothing, because the messages say "charge" or "charged". Reducing words to a shared form finds 121 (green: found; red: missed).

The rest of this part opens with an account

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