Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing Wenhan Xiongy, Jiawei Wuy, Deren Leiy, Mo Yu∗, Shiyu Chang∗, Xiaoxiao Guo∗, William Yang Wangy y University of California, Santa Barbara ∗ IBM Research fxwhan,
[email protected],
[email protected], fshiyu.chang,
[email protected] Abstract Context Types y Existing entity typing systems usually ex- person , televi- ? ploit the type hierarchy provided by knowl- Big Show then appeared at One Night sion program edge base (KB) schema to model label cor- Stand, attacking Tajiri, Super Crazy, and the Full Blooded Italians after person, athlete, relations and thus improve the overall perfor- their tag team match wrestler, mance. Such techniques, however, are not di- entertainer rectly applicable to more open and practical scenarios where the type set is not restricted The womens pole vault at the 2010 monthy, event? by KB schema and includes a vast number IAAF World Indoor Championships of free-form types. To model the underly- was held at the ASPIRE Dome on 12 ing label correlations without access to man- and 14 March. date, month ually annotated label structures, we introduce a novel label-relational inductive bias, repre- Table 1: Examples of inconsistent predictions pro- sented by a graph propagation layer that effec- duced by existing entity typing system that does not tively encodes both global label co-occurrence model label correlations. We use different subscript statistics and word-level similarities. On a symbols to indicate contradictory type pairs and show large dataset with over 10,000 free-form types, the ground-truth types in italics.