# Neural Architectures for Fine-grained Entity Type Classification

arXiv: https://arxiv.org/abs/1606.01341 · HTML: https://mlsanity.com/paper/1606.01341/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2016.

## Built on by (most cited first, 15 of 15)

- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [What the DAAM: Interpreting Stable Diffusion Using Cross Attention](https://mlsanity.com/paper/2210.04885/) (2022)
- [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021)
- [Modeling Fine-Grained Entity Types with Box Embeddings](https://mlsanity.com/paper/2101.00345/) (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)
- [TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval](https://mlsanity.com/paper/2204.08173/) (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)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [Cross-lingual Inference with A Chinese Entailment Graph](https://mlsanity.com/paper/2203.06264/) (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)
- [Denoising Enhanced Distantly Supervised Ultrafine Entity Typing](https://mlsanity.com/paper/2210.09599/) (2022)
- [Cross-Lingual Fine-Grained Entity Typing](https://mlsanity.com/paper/2110.07837/) (2021)

## Abstract (arXiv)

In this work, we investigate several neural network architectures for fine-grained entity type classification. Particularly, we consider extensions to a recently proposed attentive neural architecture and make three key contributions. Previous work on attentive neural architectures do not consider hand-crafted features, we combine learnt and hand-crafted features and observe that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism is capable of learning to attend over syntactic heads and the phrase containing the mention, where both are known strong hand-crafted features for our task. We enable parameter sharing through a hierarchical label encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We establish that the choice of training data has a drastic impact on performance, with decreases by as much as 9.85% loose micro F1 score for a previously proposed method. Despite this, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well- established FIGER (GOLD) dataset.