Finer Grained Entity Typing with TypeNet

Autor:	Murty, Shikhar, Verga, Patrick, Vilnis, Luke, McCallum, Andrew
Rok vydání:	2017
Předmět:	Computer Science - Computation and Language Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1711.05795 Zobrazit plný text záznamu View this record from Arxiv