Joint training for open-domain extraction on the web: exploiting overlap when supervision is limited

We consider the problem of jointly training structured models for extraction from multiple web sources whose records enjoy partial content overlap. This has important applications in open-domain extraction, e.g. a user materializing a table of interest from multiple relevant unstructured sources; or a site like Freebase augmenting an incomplete relation by extracting more rows from web sources. Such applications require extraction over arbitrary domains, so one cannot use a pre-trained extractor or demand a huge labeled dataset. We propose to overcome this lack of supervision by using content overlap across the related web sources. Existing methods of exploiting overlap have been developed under settings that do not generalize easily to the scale and diversity of overlap seen on Web sources.

We present an agreement-based learning framework that jointly trains the models by biasing them to agree on the agreement regions, i.e. shared text segments. We present alternatives within our framework to trade-off tractability, robustness to noise, and extent of agreement enforced; and propose a scheme of partitioning agreement regions that leads to efficient training while maximizing overall accuracy. Further, we present a principled scheme to discover low-noise agreement regions in unlabeled data across multiple sources.

Through extensive experiments over 58 different extraction domains, we establish that our framework provides significant boosts over uncoupled training, and scores over alternatives such as collective inference, staged training, and multi-view learning.