# Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

arXiv: https://arxiv.org/abs/1807.05127 · HTML: https://mlsanity.com/paper/1807.05127/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## 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)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (2021)
- [Knowledge-Rich Self-Supervision for Biomedical Entity Linking](https://mlsanity.com/paper/2112.07887/) (2021)
- [Fine-grained Entity Typing via Label Reasoning](https://mlsanity.com/paper/2109.05744/) (2021)
- [Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance Generation](https://mlsanity.com/paper/2206.13746/) (2022)
- [Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy](https://mlsanity.com/paper/2305.12802/) (2023)
- [Improving Zero-Shot Entity Retrieval through Effective Dense Representations](https://mlsanity.com/paper/2103.04156/) (2021)
- [Financial data analysis application via multi-strategy text processing](https://mlsanity.com/paper/2204.11394/) (2022)
- [Biomedical Interpretable Entity Representations](https://mlsanity.com/paper/2106.09502/) (2021)
- [Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology](https://mlsanity.com/paper/2101.10587/) (2021)
- [Hyperbolic Disentangled Representation for Fine-Grained Aspect Extraction](https://mlsanity.com/paper/2112.09215/) (2021)
- [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)
- [COMBO: A Complete Benchmark for Open KG Canonicalization](https://mlsanity.com/paper/2302.03905/) (2023)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking](https://mlsanity.com/paper/2111.06012/) (2021)
- [Cluster-based Mention Typing for Named Entity Disambiguation](https://mlsanity.com/paper/2109.11389/) (2021)
- [Hierarchical Event Grounding](https://mlsanity.com/paper/2302.04197/) (2023)

## Abstract (arXiv)

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.