# FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space

arXiv: https://arxiv.org/abs/2101.11212 · HTML: https://mlsanity.com/paper/2101.11212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [Hyperbolic Graph Convolutional Neural Networks](https://mlsanity.com/paper/1910.12933/) (2019)
- [Ultra-Fine Entity Typing](https://mlsanity.com/paper/1807.04905/) (2018)
- [Embedding Text in Hyperbolic Spaces](https://mlsanity.com/paper/1806.04313/) (2018)
- [Context-Dependent Fine-Grained Entity Type Tagging](https://mlsanity.com/paper/1412.1820/) (2014)
- [Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding](https://mlsanity.com/paper/1602.05307/) (2016)
- [Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss](https://mlsanity.com/paper/1803.03378/) (2018)
- [Noise Mitigation for Neural Entity Typing and Relation Extraction](https://mlsanity.com/paper/1612.07495/) (2016)
- [Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations](https://mlsanity.com/paper/2004.03554/) (2020)
- [Fine-Grained Entity Typing in Hyperbolic Space](https://mlsanity.com/paper/1906.02505/) (2019)
- [An Attentive Neural Architecture for Fine-grained Entity Type Classification](https://mlsanity.com/paper/1604.05525/) (2016)
- [Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings](https://mlsanity.com/paper/1702.06709/) (2017)

## Abstract (arXiv)

Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context. While distant supervision is the most common way to acquire supervised training data, it brings in label noise, as it assigns type labels to the entity mentions irrespective of mentions context. In attempts to deal with the label noise, leading research on the FG-NET assumes that the fine-grained entity typing data possesses a euclidean nature, which restraints the ability of the existing models in combating the label noise. Given the fact that the fine-grained type hierarchy exhibits a hierarchical structure, it makes hyperbolic space a natural choice to model the FG-NET data. In this research, we propose FGNET-RH, a novel framework that benefits from the hyperbolic geometry in combination with the graph structures to perform entity typing in a performance-enhanced fashion. FGNET-RH initially uses LSTM networks to encode the mention in relation with its context, later it forms a graph to distill/refine the mention encodings in the hyperbolic space. Finally, the refined mention encoding is used for entity typing. Experimentation using different benchmark datasets shows that FGNET-RH improves the performance on FG-NET by up to 3.5-% in terms of strict accuracy.