Data Cleaning publication categorizer

Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER.

Entity resolution is a very common Information Quality (IQ)
problem with many different applications. In digital libraries,
it is related to problems of citation matching and author
name disambiguation; in Natural Language Processing, it is
related to coreference matching and object identity; in Web
application, it is related to Web page disambiguation. The
problem of Entity Resolution arises because objects/entities
in real world datasets are often referred to by descriptions,
which might not be unique identifiers of these entities, lead-

This paper describes an efficient approach to record linkage.
Given two lists of records, the record-linkage problem
consists of determining all pairs that are similar to each
other, where the overall similarity between two records is
defined based on domain-specific similarities over individual
attributes constituting the record. The record-linkage
problem arises naturally in the context of data cleansing
that usually precedes data analysis and mining. We explore
a novel approach to this problem. For each attribute
of records, we first map values to a multidimensional