keyword matching or vector space model(VSM) with basic TF-IDF weighting.
Each document := a vector of term weights
Util-0.339 power-0.329 megawatt-0.309 electr-0.217 energi-0.206 california 0.18 ….
Each weight := degree of association between the document and the terms
Most term weighting schema is TF-IDF (Term Frequency-IDF := Inverse Document Frequency) based on empirical observations regarding text(Salton, G. 1989).
rare terms are not less relevant than frequent terms(IDF assumption)
multiple occurrences of a term in a document are not less relevant than single occurrences(TF assumption)
Long documents are not preferred to short documents(normalization assumption)