Inna Gelfer Kalmanovich

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3ranked-venue papers

1as first author

0since 2021 · last 2011

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval

2 papers

Information retrieval · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Information retrieval › document retrieval cluster-based retrieval	0.1	1	2009	Integrating clusters created offline with query-specific clusters for document retrieval · SIGIR 2009
Information retrieval › relevance feedback pseudo-relevance feedback	0.1	1	2009	Cluster-based query expansion · SIGIR 2009
Information retrieval › query reformulation query expansion	0.1	1	2009	Cluster-based query expansion · SIGIR 2009
Information retrieval › retrieval models › language model relevance model	0.0	1	2009	Cluster-based query expansion · SIGIR 2009
Information retrieval retrieval models	0.0	1	2009	Cluster-based query expansion · SIGIR 2009

Methods — techniques the papers use, named apart from their topics

document clustering · 0.2

Year	Publication	Venue	Position
2011	Re-ranking search results using an additional retrieved list Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich	Inf. Retr.	3
2009	Cluster-based query expansion abstract We demonstrate the merits of using document clusters that are created offline to improve the overall effectiveness and performance robustness of a state-of-the-art pseudo-feedback-based query expansion method -- the relevance model. Inna Gelfer Kalmanovich, Oren Kurland	SIGIR	1
2009	Integrating clusters created offline with query-specific clusters for document retrieval abstract Previous work on cluster-based document retrieval has used either static document clusters that are created offline, or query-specific (dynamic) document clusters that are created from top-retrieved documents. We present the potential merit of integrating these two types of clusters. Lior Meister, Oren Kurland, Inna Gelfer Kalmanovich	SIGIR	3