Mining Document Collections to Facilitate Accurate Approximate Entity Matching

43 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “A popular generalization of this framework, which combines neighborhood information with latent factor approach [18], leads to the following model: where µ is the over all constant mean rating value, b i and b j are user and item biases, respectively.”
Asymmetric Minwise Hashing for Indexing Binary Inner Products and Set Containment (2015, methodology): “3 Shortcomings of Inverted Index Based Approaches for MIPS (and MCS) Owing to its practical significance, there have been many existing heuristics for solving the MIPS (or MCS) problem [31, 34, 12].”
Asymmetric Minwise Hashing (2014, methodology): “Owing to its practical signiﬁcance, there have been many existing heuristics for solving the MIPS (or MCS) problem [30, 33, 12].”
On indexing error-tolerant set containment (2010, methodology): “A closely related area is that of exact set containment indexing [11, 9, 24, 25, 28].”
Frameworks for entity matching: A comparison (2010, background): “Similarity computation may also utilize different kinds of auxiliary information, such as dictionaries, thesauri or domain-specific lookup tables [14], e.g., to deal with synonyms, homonyms, abbreviations, acronyms, or geographic name variations.”