tailieunhanh - Query Operations Relevance Feedback & Query Expansion

After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved documents. Use this feedback information to reformulate the query. Produce new results based on reformulated query. Allows more interactive, multi-pass process. | Query Operations Relevance Feedback & Query Expansion Relevance Feedback After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved documents. Use this feedback information to reformulate the query. Produce new results based on reformulated query. Allows more interactive, multi-pass process. Relevance Feedback Architecture Rankings IR System Document corpus Ranked Documents 1. Doc1 2. Doc2 3. Doc3 . . 1. Doc1 2. Doc2 3. Doc3 . . Feedback Query String Revised Query ReRanked Documents 1. Doc2 2. Doc4 3. Doc5 . . Query Reformulation Query Reformulation Revise query to account for feedback: Query Expansion: Add new terms to query from relevant documents. Term Reweighting: Increase weight of terms in relevant documents and decrease weight of terms in irrelevant documents. Several algorithms for query reformulation. Query Reformulation for VSR Change query vector using vector algebra. Add the . | Query Operations Relevance Feedback & Query Expansion Relevance Feedback After initial retrieval results are presented, allow the user to provide feedback on the relevance of one or more of the retrieved documents. Use this feedback information to reformulate the query. Produce new results based on reformulated query. Allows more interactive, multi-pass process. Relevance Feedback Architecture Rankings IR System Document corpus Ranked Documents 1. Doc1 2. Doc2 3. Doc3 . . 1. Doc1 2. Doc2 3. Doc3 . . Feedback Query String Revised Query ReRanked Documents 1. Doc2 2. Doc4 3. Doc5 . . Query Reformulation Query Reformulation Revise query to account for feedback: Query Expansion: Add new terms to query from relevant documents. Term Reweighting: Increase weight of terms in relevant documents and decrease weight of terms in irrelevant documents. Several algorithms for query reformulation. Query Reformulation for VSR Change query vector using vector algebra. Add the vectors for the relevant documents to the query vector. Subtract the vectors for the irrelevant docs from the query vector. This both adds both positive and negatively weighted terms to the query as well as reweighting the initial terms. Optimal Query Assume that the relevant set of documents Cr are known. Then the best query that ranks all and only the relevant queries at the top is: Where N is the total number of documents. Standard Rochio Method Since all relevant documents unknown, just use the known relevant (Dr) and irrelevant (Dn) sets of documents and include the initial query q. : Tunable weight for initial query. : Tunable weight for relevant documents. : Tunable weight for irrelevant documents. Ide Regular Method Since more feedback should perhaps increase the degree of reformulation, do not normalize for amount of feedback: : Tunable weight for initial query. : Tunable weight for relevant documents. : Tunable weight for irrelevant documents. Ide “Dec .

• Tiếng Anh thương mại
• Query Operations
• Relevance Feedback
• Query Expansion
• Query Reformulation
• Term Reweighting
• Evaluating Relevance Feedback
• Database Management Systems
• Algorithms for Query Processing and Optimization
• Query Processing
• Translating SQL Queries into Relational Algebra
• Algorithms for External Sorting
• Algorithms for SELECT and JOIN Operations
• Object oriented database development
• The importance of OODBMS products
• Object defininition language
• Object query language
• Defining operations
• Defining relationships