پاورپوینت کامل Chapter 19: Information Retrieval 29 اسلاید در PowerPoint

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پاورپوینت کامل Chapter 19: Information Retrieval 29 اسلاید در PowerPoint

اسلاید ۴: Information Retrieval Systems (Cont.)Differences from database systemsIR systems don’t deal with transactional updates (including concurrency control and recovery)Database systems deal with structured data, with schemas that define the data organizationIR systems deal with some querying issues not generally addressed by database systemsApproximate searching by keywordsRanking of retrieved answers by estimated degree of relevance

اسلاید ۵: Keyword SearchIn full text retrieval, all the words in each document are considered to be keywords. We use the word term to refer to the words in a documentInformation-retrieval systems typically allow query expressions formed using keywords and the logical connectives and, or, and notAnds are implicit, even if not explicitly specifiedRanking of documents on the basis of estimated relevance to a query is criticalRelevance ranking is based on factors such asTerm frequencyFrequency of occurrence of query keyword in documentInverse document frequencyHow many documents the query keyword occurs in Fewer give more importance to keywordHyperlinks to documentsMore links to a document document is more important

اسلاید ۶: Relevance Ranking Using TermsTF-IDF (Term frequency/Inverse Document frequency) ranking:Let n(d) = number of terms in the document dn(d, t) = number of occurrences of term t in the document d.Relevance of a document d to a term t The log factor is to avoid excessive weight to frequent termsRelevance of document to query Qn(d)n(d, t)1 +TF (d, t) = logr (d, Q) =TF (d, t)n(t)tQ

اسلاید ۷: Relevance Ranking Using Terms (Cont.)Most systems add to the above modelWords that occur in title, author list, section headings, etc. are given greater importanceWords whose first occurrence is late in the document are given lower importanceVery common words such as “a”, “an”, “the”, “it” etc are eliminatedCalled stop wordsProximity: if keywords in query occur close together in the document, the document has higher importance than if they occur far apartDocuments are returned in decreasing order of relevance scoreUsually only top few documents are returned, not all

اسلاید ۸: Similarity Based RetrievalSimilarity based retrieval – retrieve documents similar to a given documentSimilarity may be defined on the basis of common wordsE.g. find k terms in A with highest TF (d, t ) / n (t ) and use these terms to find relevance of other documents.Relevance feedback: Similarity can be used to refine answer set to keyword queryUser selects a few relevant documents from those retrieved by keyword query, and system finds other documents similar to theseVector space model: define an n-dimensional space, where n is the number of words in the document set.Vector for document d goes from origin to a point whose i th coordinate is TF (d,t ) / n (t )The cosine of the angle between the vectors of two documents is used as a measure of their similarity.

اسلاید ۹: Relevance Using HyperlinksNumber of documents relevant to a query can be enormous if only term frequencies are taken into accountUsing term frequencies makes “spamming” easyE.g. a travel agency can add many occurrences of the words “travel” to its page to make its rank very highMost of the time people are looking for pages from popular sitesIdea: use popularity of Web site (e.g. how many people visit it) to rank site pages that match given keywordsProblem: hard to