Intuition behind tf-idf for term extraction
<p>I'm trying to build a dictionary of words using <em>tf-idf</em>. However, intuitively it doesn't make sense.</p>

<p>If the <em>inverse document frequency</em> (<em>idf</em>) part of <em>tf-idf</em> calculates the relevance of a term with respect to entire corpus, then that implies some of the important words might have a lower relevance.</p>

<p>If we look at a corpus of legal documents, a term like "license" or "legal" might occur in every document. Due to <em>idf</em>, the score for these terms will be very low. However, intuitively speaking, these terms should have a higher score since these are clearly legal terms. </p>

<p>Is <em>tf-idf</em> a bad approach for building a dictionary of terms?</p>