# An Attentive Neural Architecture for Fine-grained Entity Type Classification

arXiv: https://arxiv.org/abs/1604.05525 · HTML: https://mlsanity.com/paper/1604.05525/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2016.

## Built on by (most cited first, 6 of 6)

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- [Denoising Enhanced Distantly Supervised Ultrafine Entity Typing](https://mlsanity.com/paper/2210.09599/) (2022)
- [FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space](https://mlsanity.com/paper/2101.11212/) (2021)
- [Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction](https://mlsanity.com/paper/2305.15520/) (2023)

## Abstract (arXiv)

In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state-of-the-art performance with 74.94% loose micro F1-score on the well-established FIGER dataset, a relative improvement of 2.59%. We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity.