# DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence

arXiv: https://arxiv.org/abs/2511.10404 · HTML: https://mlsanity.com/paper/2511.10404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 13 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Named Entity Recognition and Classification on Historical Documents: A Survey](https://mlsanity.com/paper/2109.11406/) (2021)
- [YAGO 4.5: A Large and Clean Knowledge Base with a Rich Taxonomy](https://mlsanity.com/paper/2308.11884/) (2023)
- [ArCo: the Italian Cultural Heritage Knowledge Graph](https://mlsanity.com/paper/1905.02840/) (2019)
- [LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking](https://mlsanity.com/paper/2407.04020/) (2024)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [On the Trustworthiness of Tree Ensemble Explainability Methods](https://mlsanity.com/paper/2110.00086/) (2021)
- [Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation](https://mlsanity.com/paper/2505.02737/) (2025)
- [DeepMEL: A Multi-Agent Collaboration Framework for Multimodal Entity Linking](https://mlsanity.com/paper/2508.15876/) (2025)
- [Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone](https://mlsanity.com/paper/2505.20113/) (2025)

## Abstract (arXiv)

In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the field of humanities due to complex document typologies, lack of domain-specific datasets and models, and long-tail entities, i.e., entities under-represented in Knowledge Bases (KBs). The goal of this paper is to address these issues with two main contributions. The first contribution is DELICATE, a novel neuro-symbolic method for EL on historical Italian which combines a BERT-based encoder with contextual information from Wikidata to select appropriate KB entities using temporal plausibility and entity type consistency. The second contribution is ENEIDE, a multi-domain EL corpus in historical Italian semi-automatically extracted from two annotated editions spanning from the 19th to the 20th century and including literary and political texts. Results show how DELICATE outperforms other EL models in historical Italian even if compared with larger architectures with billions of parameters. Moreover, further analyses reveal how DELICATE confidence scores and features sensitivity provide results which are more explainable and interpretable than purely neural methods.