Target-language score
Exact lexical overlap
The score answers one narrow question: how many extracted terms from this job description also appear as exact normalized tokens in this resume?
Normalize
Lowercase both texts. Replace characters outside letters, digits, plus signs, hashes, spaces, and hyphens with spaces.
Remove noise
Discard tokens of three characters or fewer and the published common-word list below.
Choose 14
Count job-description tokens. Keep up to 14, ordered by frequency and then alphabetically when counts tie.
Match exactly
A target term matches when the same normalized token exists anywhere in the resume corpus.
Calculate
Divide matched target terms by all extracted target terms, multiply by 100, then round to the nearest whole number.
score = round(matched target terms ÷ extracted target terms × 100)If the job description yields no target terms, the score is zero.
Removed common words
20 published filters
about · after · also · been · being · from · have · into · more · that · their · them · they · this · through · using · will · with · work · your
Bullet signals
Two checks. Nothing inferred.
A trimmed, lowercased bullet passes when it begins with one of the published verbs.
achieved · built · created · delivered · designed · drove · grew · improved · launched · led · managed · reduced · shipped · simplified · scaledA bullet passes when it contains any digit or one of these symbols. Presence does not prove the number is meaningful or true.
0—9 · % · $ · £ · €Known limitations
What the method cannot know
- Synonyms or equivalent skills that use different words.
- Whether a missing term is relevant to the candidate’s real experience.
- Whether a number represents an outcome, a date, a team size, or noise.
- Whether a bullet is accurate, attributable, well written, or persuasive.
- How any recruiter, employer, or applicant-tracking system will decide.