M
Michael Herscovici
Researcher at IBM
Publications - 20
Citations - 1402
Michael Herscovici is an academic researcher from IBM. The author has contributed to research in topics: Pruning (decision trees) & Search engine indexing. The author has an hindex of 13, co-authored 20 publications receiving 1359 citations.
Papers
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Proceedings ArticleDOI
Efficient query evaluation using a two-level retrieval process
TL;DR: An efficient query evaluation method based on a two level approach that significantly reduces the total number of full evaluations by more than 90%, almost without any loss in precision or recall.
Proceedings ArticleDOI
Static index pruning for information retrieval systems
David Carmel,Doron Cohen,Ronald Fagin,Eitan Farchi,Michael Herscovici,Yoelle Maarek,Aya Soffer +6 more
TL;DR: In this article, the authors introduce static index pruning methods that significantly reduce the index size in information retrieval systems and investigate uniform and term-based methods that each remove selected entries from the index and yet have only a minor effect on retrieval results.
Patent
Information search using knowledge agents
TL;DR: In this article, a method for searching a corpus of documents, such as the World Wide Web, includes defining a knowledge domain and identifying a set of reference documents in the corpus pertinent to the domain.
Patent
Automatic query routing and rank configuration for search queries in an information retrieval system
TL;DR: In this paper, a query is received and parsed to generate a set of query terms, and statistical information is identified regarding each of the query terms and different permutations of the queries.
Patent
Indexing and searching of electronic message transmission thread sets
Andrei Z. Broder,Nadav Eiron,Marcus Fontoura,Michael Herscovici,Ronny Lempel,John McPherson,Eugene J. Shekita +6 more
TL;DR: In this article, a thread processor analyzes the EMT threads and records the thread configuration data, and a query manager utilizes the thread configurations data to conduct selective searches of EMT volume.