DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence

TL;DR

This paper introduces DELICATE, a neuro-symbolic entity linking method for historical Italian that leverages temporal and contextual information, along with ENEIDE, a new multi-domain corpus, improving accuracy and interpretability in challenging humanities contexts.

Contribution

The paper presents DELICATE, a novel neuro-symbolic approach combining BERT and knowledge base information for entity linking, and introduces ENEIDE, a new annotated corpus for historical Italian.

Findings

DELICATE outperforms existing models in historical Italian EL.

Confidence scores and features enhance explainability and interpretability.

DELICATE achieves competitive results with larger neural architectures.

Abstract

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.