Occurrence Statistics of Entities, Relations and Types on the Web
This work addresses the challenge of reliable entity occurrence estimation for web-scale applications, but it appears incremental as it builds on existing techniques like maximum mean discrepancy.
The authors tackled the problem of estimating entity occurrence statistics on the open web, where distribution mismatches between training data and web data degrade model performance, by proposing the use of maximum mean discrepancy for estimation.
The problem of collecting reliable estimates of occurrence of entities on the open web forms the premise for this report. The models learned for tagging entities cannot be expected to perform well when deployed on the web. This is owing to the severe mismatch in the distributions of such entities on the web and in the relatively diminutive training data. In this report, we build up the case for maximum mean discrepancy for estimation of occurrence statistics of entities on the web, taking a review of named entity disambiguation techniques and related concepts along the way.