lecture6-tfidf-handout-6-per

Lecture6-tfidf-handout-6-per

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: . Introduc)on to Informa)on Retrieval Problem with Boolean search: feast or famine Ch. 6   Boolean queries o]en result in either too few (=0) or too many (1000s) results.   Query 1: standard user dlink 650 → 200,000 hits   Query 2: standard user dlink 650 no card found : 0 hits   It takes a lot of skill to come up with a query that produces a manageable number of hits.   AND gives too few; OR gives too many Introduc)on to Informa)on Retrieval Ranked retrieval models   Rather than a set of documents sa*sfying a query expression, in ranked retrieval, the system returns an ordering over the (top) documents in the collec*on for a query   Free text queries: Rather than a query language of operators and expressions, the user s query is just one or more words in a human language   In principle, there are two separate choices here, but in prac*ce, ranked retrieval has normally been associated with free text queries and vice versa 6 1 Introduc)on to Informa)on Retrieval Feast or famine: not a problem in ranked retrieval Ch. 6   Indeed, the size of the result set is not an issue   We just show the top k ( ≈ 10) results   We don t overwhelm the user   Premise: the ranking algorithm works Ch. 6 Scoring as the basis of ranked retrieval   When a system produces a ranked result set, large result sets are not an issue Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Ch. 6   We wish to return in order the documents most likely to be useful to the searcher   How can we rank ­order the documents in the collec*on with respect to a query?   Assign a score – say in [0, 1] – to each document   This score...
View Full Document

This document was uploaded on 02/26/2014.

Ask a homework question - tutors are online