Relevance modeling with multiple query Variations

Xiaolu Lu, Oren Kurland, J. Shane Culpepper, Nick Craswell, Ofri Rom

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

The generative theory for relevance and its operational manifestation-the relevance model-are based on the premise that a single query is used to represent an information need for retrieval. In this work, we extend the theory and devise novel techniques for relevance modeling using as set of query variations representing the same information need. Our new approach is based on fusion at the term level, the model level, or the document-list level.We theoretically analyze the connections between these methods and provide empirical support of their equivalence using TREC datasets. Specifically, our new approach of inducing relevance models from multiple query variations substantially outperforms relevance model induction from a single query which is the standard practice. Our approach also outperforms fusion over multiple query variations, which is currently one of the best known baselines for several commonly used test collections.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفICTIR 2019 - Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval
الصفحات27-34
عدد الصفحات8
رقم المعيار الدولي للكتب (الإلكتروني)9781450368810
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 23 سبتمبر 2019
الحدث9th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2019 - Santa Clara, الولايات المتّحدة
المدة: ٢ أكتوبر ٢٠١٩٥ أكتوبر ٢٠١٩

سلسلة المنشورات

الاسمICTIR 2019 - Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

!!Conference

!!Conference9th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2019
الدولة/الإقليمالولايات المتّحدة
المدينةSanta Clara
المدة٢/١٠/١٩٥/١٠/١٩

All Science Journal Classification (ASJC) codes

  • !!Computer Science (miscellaneous)
  • !!Information Systems

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