The problem of entity-typing has been studied predominantly in supervised
learning fashion, mostly with task-specific annotations (for coarse types) and
sometimes with distant supervision (for fine types). While such approaches have
strong performance within datasets, they often lack the flexibility to transfer
across text genres and to generalize to new type taxonomies. In this work we
propose a zero-shot entity typing approach that requires no annotated data and
can flexibly identify newly defined types. Given a type taxonomy defined as
Boolean functions of FREEBASE "types", we ground a given mention to a set of
type-compatible Wikipedia entries and then infer the target mention's types
using an inference algorithm that makes use of the types of these entries. We
evaluate our system on a broad range of datasets, including standard
fine-grained and coarse-grained entity typing datasets, and also a dataset in
the biological domain. Our system is shown to be competitive with
state-of-the-art supervised NER systems and outperforms them on out-of-domain
datasets. We also show that our system significantly outperforms other
zero-shot fine typing systems.

本文提出了一种无监督的零样本实体类型的方法，该方法可以弹性地识别新定义的类型，并利用 FREEBASE 的布尔函数进行自我推理。我们在包括生物领域的各种数据集上评估了该系统，并证明了它在超出领域的数据集上优于监督学习方法与其他零样本细粒度关键词方法。