| Challenge: | Existing approaches to multilingual entity linking are cross-lingual, with a focus on zero-shot evaluation. |
| Approach: | They propose a new formulation for multilingual entity linking where language-specific mentions resolve to a language-agnostic Knowledge Base. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large multilingual dataset and shows that frequency-based analysis provided key insights for the model and training enhancements. |
Similar Papers
Multi-lingual Entity Discovery and Linking (P18-5)
Copied to clipboard
| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |
Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)
Copied to clipboard
| Challenge: | Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision . |
| Approach: | They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly. |
| Outcome: | The proposed approach significantly improves on the current state-of-the-art in 8 languages. |
Towards Zero-resource Cross-lingual Entity Linking (D19-61)
Copied to clipboard
| Challenge: | XEL is challenging for most languages because of limited availability of requisite resources . simulated environments that use significant resources are not available in truly low-resource languages . |
| Approach: | They propose improvements to entity candidate generation and disambiguation to make better use of the limited resources available in low-resource languages. |
| Outcome: | The proposed model gains 6-20% end-to-end linking accuracy on four low-resource languages. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
| Outcome: | The proposed model performs well in both monolingual and multilingual settings. |
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (N18-5)
Copied to clipboard
| Challenge: | ELISA-EDL is a cross-lingual entity extraction, linking and localization system for Wikipedia languages. |
| Approach: | They propose a cross-lingual entity extraction, linking and localization system for English speakers . it extracts entities from unstructured text in any of 282 Wikipedia languages and links them to English knowledge bases . |
| Outcome: | The proposed system extracts entity mentions from Wikipedia and links them to English knowledge bases and visualizes locations related to disaster topics on a world heatmap. |
Design Challenges in Low-resource Cross-lingual Entity Linking (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing techniques for grounding mentions of entities in a foreign language do not rise to the challenges introduced by text in low-resource languages (LRL) and fail to generalize to text not taken from Wikipedia, on which they are usually trained. |
| Approach: | They propose a cross-lingual XEL technique that uses search engines to locate and search for foreign language entries in Wikipedia. |
| Outcome: | The proposed system shows an increase of 25% in gold candidate recall and 13% in end-to-end linking accuracy over state-of-the-art baselines. |
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning (2022.findings-acl)
Copied to clipboard
| Challenge: | a new method for named entity linking is being developed in the field of public health . it uses an offline unsupervised construction of a translated dictionary and a pre-trained transformer language model to filter candidates according to context. |
| Approach: | They propose a method for mapping mentions in a source language to UMLS concepts . they extend an offline unsupervised translation of a translated UMLS dictionary . |
| Outcome: | The proposed approach achieves state-of-the-art on the Hebrew Camoni corpus and English datasets. |
Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks (2021.naacl-main)
Copied to clipboard
| Challenge: | Masked language models have become the de facto standard when processing text . however, these models are evaluated in a monolingual setting only . |
| Approach: | They propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap between different languages. |
| Outcome: | The proposed approach bridges the gap between word representations and knowledge graphs by using a shared vocabulary of entities. |
Zero-Shot Cross-lingual Name Retrieval for Low-Resource Languages (D19-61)
Copied to clipboard
| Challenge: | a novel name retrieval method is proposed for languages with no annotations or training data. |
| Approach: | They propose a method which relies on zero annotation or resources from the target language . they pre-train an orthographic encoder using Wikipedia inter-lingual links from dozens of languages . |
| Outcome: | The proposed method shows 11.6% improvement over state-of-the-art methods. |