  Entity resolution (ER) is one of the fundamental problems in data
integration, where machine learning (ML) based classifiers often provide the
state-of-the-art results. Considerable human effort goes into feature
engineering and training data creation. In this paper, we investigate a new
problem: Given a dataset D_T for ER with limited or no training data, is it
possible to train a good ML classifier on D_T by reusing and adapting the
training data of dataset D_S from same or related domain? Our major
contributions include (1) a distributed representation based approach to encode
each tuple from diverse datasets into a standard feature space; (2)
identification of common scenarios where the reuse of training data can be
beneficial; and (3) five algorithms for handling each of the aforementioned
scenarios. We have performed comprehensive experiments on 12 datasets from 5
different domains (publications, movies, songs, restaurants, and books). Our
experiments show that our algorithms provide significant benefits such as
providing superior performance for a fixed training data size.
