# Hierarchical Entity Typing via Multi-level Learning to Rank

arXiv: https://arxiv.org/abs/2004.02286 · HTML: https://mlsanity.com/paper/2004.02286/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 18 of 18)

- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [LOME: Large Ontology Multilingual Extraction](https://mlsanity.com/paper/2101.12175/) (2021)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference](https://mlsanity.com/paper/2202.06167/) (2022)
- [A Unified View of Evaluation Metrics for Structured Prediction](https://mlsanity.com/paper/2310.13793/) (2023)
- [Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation](https://mlsanity.com/paper/2206.13746/) (2022)
- [Generative Entity Typing with Curriculum Learning](https://mlsanity.com/paper/2210.02914/) (2022)
- [Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy](https://mlsanity.com/paper/2305.12802/) (2023)
- [Towards Better Document-level Relation Extraction via Iterative Inference](https://mlsanity.com/paper/2211.14470/) (2022)
- [Cross-lingual Inference with A Chinese Entailment Graph](https://mlsanity.com/paper/2203.06264/) (2022)
- [Asking the Right Questions in Low Resource Template Extraction](https://mlsanity.com/paper/2205.12643/) (2022)
- [Ontology Enrichment for Effective Fine-grained Entity Typing](https://mlsanity.com/paper/2310.07795/) (2023)
- [Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing](https://mlsanity.com/paper/2208.10081/) (2022)
- [Hierarchical Event Grounding](https://mlsanity.com/paper/2302.04197/) (2023)
- [Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning](https://mlsanity.com/paper/2310.14596/) (2023)
- [Agentic Meta-Orchestrator for Multi-task Copilots](https://mlsanity.com/paper/2510.22781/) (2025)

## Abstract (arXiv)

We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction. At training, our novel multi-level learning-to-rank loss compares positive types against negative siblings according to the type tree. During prediction, we define a coarse-to-fine decoder that restricts viable candidates at each level of the ontology based on already predicted parent type(s). We achieve state-of-the-art across multiple datasets, particularly with respect to strict accuracy.