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Designing an Effective Relevance Feedback System with Genetic Algorithms

gil-rasmussen avatar
10 diapositives · 243 Views · Nov 18, 2014

This article explores the development of a relevance feedback system enhanced by genetic algorithms. It discusses the importance of relevance judgments—both positive and negative—and strategies for applying these judgments to queries and documents. The approach involves modifying term weights based on relevance and introduces query variants to optimize retrieval results efficiently within 3-4 iterations. It also highlights the need for user satisfaction, ensuring that users do not encounter repetitive documents during the process. Finally, the role of natural language processing techniques and citation processing in enhancing retrieval effectiveness is examined.

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Designing an Effective Relevance Feedback System with Genetic Algorithms
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  1. Relevance Feedback • Limitations • Must yield result within at most 3-4 iterations • Users will likely terminate the process sooner • User may get irritated at seeing same documents repeated after every iteration • It has proven to increase the effectiveness of retrieval

  2. Designing a Relevance Feedback System • Use positive or negative relevance judgments • Where to apply relevance judgments (query, profile, document, retrieval algorithm) • Term weight modification. E.g., • Increase the weight for terms that appear in relevant docs • Add new terms found in relevant docs that are frequently mention in connection with query term

  3. Genetic Algorithms • Several possible solutions are generated in parallel • The best few of these solutions is chosen and replicated, while the poor ones eliminated • Replicated solutions creates a breeding population, from which new solutions arise • The breeding is accomplished by by an exchange of some of the characteristics of the chosen solutions in a crossover operation

  4. Genetic Algorithms (cont.) • Hill climbing is avoided by • Pursue multiple solutions in parallel, and discard the low hills • Introduce new characteristic values at low rate through mutation process (random exchange) • Relevance Feedback • Relieves the user of the burden of assigning term weights • Begins with no weights. Generates query variants by assigning term weights randomly

  5. Genetic Algorithms (cont.) • Query variants are vector of query term weights • Each query variant is used to search the documents in the database • Evaluate each variant with equation on pg. 226 • The variants with highest value creating the most replications • The resulting breeding population is developed to the same size as the original population

  6. Natural Language Processing • Focus on structure more than meaning, consequently problems are • Syntactic ambiguity e.g., they are visiting relatives • Deep structure of a sentence e.g., grace • May or may not be semantically correct e.g., Colorless green ideas sleep furiously • Syntactic rules do not apply to e.g., boolean queries

  7. Natural Language Processing (cont.) • Semantic Analysis • Even more elusie e.g., red herring, carrying coals to Newcastle • Techniques for Semantic Analysis • Latent semantic indexing uses multidimensional scaling methods to identify concepts • Dialogue Analysis involves interaction that each time clarifies further what is to be retrieved

  8. Citation Processing • Use of cited documents to enhance the description of a primary document • Some use co-citation as a measure of document similarity I.e., number of papers that cite both • Bibliographic coupling, when two documents cite the same document • Design problems: Locating citations, interpretation, eliminate duplicate/useless,

  9. Hypertext Links • Means of connecting 2 distinct pieces of text • Consists of an identifier and a pointer • Possibly aid retrieval by suggesting hyperlinks given in top ranked document retrieved • Do not follow links from linked documents • Information Filtering: Eliminate large segments of database from consideration • Passage Retrieval: Identifying relevant sections within a large document encyclopedia

  10. Image and Sound Processing • Techniques for evaluating and manipulating images directly • Voice recognition • Animation and sound: compare to those in libraries • Music can use style and then pattern matching