Existing entity typing systems usually exploit the type hierarchy provided by
knowledge base (KB) schema to model label correlations and thus improve the
overall performance. Such techniques, however, are not directly applicable to
more open and practical scenarios where the type set is not restricted by KB
schema and includes a vast number of free-form types. To model the underly-ing
label correlations without access to manually annotated label structures, we
introduce a novel label-relational inductive bias, represented by a graph
propagation layer that effectively encodes both global label co-occurrence
statistics and word-level similarities.On a large dataset with over 10,000
free-form types, the graph-enhanced model equipped with an attention-based
matching module is able to achieve a much higher recall score while maintaining
a high-level precision. Specifically, it achieves a 15.3% relative F1
improvement and also less inconsistency in the outputs. We further show that a
simple modification of our proposed graph layer can also improve the
performance on a conventional and widely-tested dataset that only includes
KB-schema types.

本文提出了一种新的标签关系归纳偏好，利用图传播层有效编码全局标签共现统计和单词级别相似性，结合基于注意力的匹配模块，可以在包括超过 10,000 种自由形式类型的大型数据集上实现更高的召回分数，并且在输出的稳定性方面也更少不一致性。