학술논문
Finer Grained Entity Typing with TypeNet
Document Type
Working Paper
Source
Subject
Language
Abstract
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is a relative lack of resources in the form of fine-grained, deep type hierarchies aligned to existing knowledge bases. In response, we introduce TypeNet, a dataset of entity types consisting of over 1941 types organized in a hierarchy, obtained by manually annotating a mapping from 1081 Freebase types to WordNet. We also experiment with several models comparable to state-of-the-art systems and explore techniques to incorporate a structure loss on the hierarchy with the standard mention typing loss, as a first step towards future research on this dataset.
Comment: Accepted at 6th Workshop on Automated Knowledge Base Construction (AKBC) at NIPS 2017
Comment: Accepted at 6th Workshop on Automated Knowledge Base Construction (AKBC) at NIPS 2017