# Finer Grained Entity Typing with TypeNet

arXiv: https://arxiv.org/abs/1711.05795 · HTML: https://mlsanity.com/paper/1711.05795/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Creating Training Sets via Weak Indirect Supervision](https://mlsanity.com/paper/2110.03484/) (2021)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [Leveraging Natural Supervision for Language Representation Learning and Generation](https://mlsanity.com/paper/2207.10617/) (2022)

## Abstract (arXiv)

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.