  We consider the problem of jointly training structured models for extraction
from sources whose instances enjoy partial overlap. This has important
applications like user-driven ad-hoc information extraction on the web. Such
applications present new challenges in terms of the number of sources and their
arbitrary pattern of overlap not seen by earlier collective training schemes
applied on two sources. We present an agreement-based learning framework and
alternatives within it to trade-off tractability, robustness to noise, and
extent of agreement. We provide a principled scheme to discover low-noise
agreement sets in unlabeled data across the sources. Through extensive
experiments over 58 real datasets, we establish that our method of additively
rewarding agreement over maximal segments of text provides the best trade-offs,
and also scores over alternatives such as collective inference, staged
training, and multi-view learning.
