Joint training for open-domain extraction on the web
Rahul Gupta, Sunita Sarawagi · 2011
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.