cs276ainformationretrieval(參考版)
2024-10-28 17:52本頁(yè)面
【正文】 Warm? Superconductors For Fast Circuit ? + 5. , 07/09/91, Soviets May Adapt Parts of SS20 Missile For Commercial Use ? 6. , 07/12/88, Gaping Gap: Pentagon Lags in Race To Match the Soviets In Rocket Launchers ? 7. , 06/14/90, Rescue of Satellite By Space Agency To Cost $90 Million ? + 8. , 12/02/87, Telemunications Tale of Two Companies Evaluation of relevance feedback strategies ? Use q0 and pute precision and recall graph ? Use qm and pute precision recall graph ? Use all documents in the collection ? Spectacular improvements, but … it?s cheating! ? Partly due to known relevant documents ranked higher ? Must evaluate with respect to documents not seen by user ? Use documents in residual collection (set of documents minus those assessed relevant) ? Measures usually lower than for original query ? More realistic evaluation ? Relative performance can be validly pared ? Empirically, one round of relevance feedback is often very useful. Two rounds is sometimes marginally useful. Relevance Feedback on the Web ? Some search engines offer a similar/related pages feature (trivial form of relevance feedback) ? Google (linkbased) ? Altavista ? Stanford web ? But some don?t because it?s hard to explain to average user: ? Alltheweb ? msn ? Yahoo ? Excite initially had true relevance feedback, but abandoned it due to lack of use. α/β/γ ?? Other Uses of Relevance Feedback ? Following a changing information need ? Maintaining an information filter (., for a news feed) ? Active learning [Deciding which examples it is most useful to know the class of to reduce annotation costs] Relevance Feedback Summary ? Relevance feedback has been shown to be effective at improving relevance of results. ? Requires enough judged documents, otherwise it?s unstable (≥ 5 remended) ? For queries in which the set of relevant documents is medium to large ? Full relevance feedback is painful for the user. ? Full relevance feedback is not very efficient in most IR systems. ? Other types of interactive retrieval may improve relevance by as much with less work. The plete landscape ? Global methods ? Query expansion/reformulation ? Thesauri (or WordNet) ? Automatic thesaurus generation ? Global indirect relevance feedback ? Local methods ? Relevance feedback ? Pseudo relevance feedback Query Reformulation: Vocabulary Tools ? Feedback ? Information about stop lists, stemming, etc. ? Numbers of hits on each term or phrase ? Suggestions ? Thesaurus ? Controlled vocabulary ? Browse lists of terms in the inverted index Query Expansion ? In relevance feedback, users give additional input (relevant/nonrelevant) on documents, which is used to reweight terms in the docume
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