| Challenge: | Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in Knowledge Graph (KG). |
| Approach: | They propose to densify the Knowledge Graph (KG) with co-occurrence statistics and then use the densified KG to train entity embeddings. |
| Outcome: | The proposed system outperforms state-of-the-art EL systems on benchmark datasets and outperformed state- of-the art systems on sparsely connected entities in the KG. |
Similar Papers
entity-linkings: A Unified Library for Entity Linking (2026.eacl-demo)
Copied to clipboard
| Challenge: | Entity linking (EL) is the task of mapping named entities in text to canonical entries in a knowledge base. |
| Approach: | They propose a unified library for using and developing entity linking systems . a strong emphasis is placed on usability, making it highly extensible . |
| Outcome: | a new library aims to disambiguate named entities in text by mapping them to canonical entries in a knowledge base. |
Fine-Grained Evaluation for Entity Linking (D19-1)
Copied to clipboard
| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
| Approach: | They propose a fine-grained categorization of different types of entity mentions and links and propose 'fuzzy recall' metric to address the lack of consensus and compare a selection of online EL systems. |
| Outcome: | The proposed task offers a bridge between unstructured text and structured KBs, where EL has applications for semantic search, document classification, relation extraction, and more. |
S2abEL: A Dataset for Entity Linking from Scientific Tables (2023.emnlp-main)
Copied to clipboard
| Challenge: | Entity linking (EL) is a longstanding problem in natural language processing and information extraction. |
| Approach: | They propose a neural baseline method for EL on scientific tables containing many out-of-knowledge-base mentions and a method that significantly outperforms a generic table EL method. |
| Outcome: | The proposed method significantly outperforms state-of-the-art generic table EL method on scientific tables with many out-of knowledge-base mentions. |
Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models. |
| Approach: | They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data. |
| Outcome: | The proposed model can be exploited to narrow the gap between EL systems trained on high and low amounts of labeled data. |
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)
Copied to clipboard
Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray
| Challenge: | Existing work deals with EL in the context of longer text, such as a sentence. |
| Approach: | They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches. |
| Outcome: | The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods. |
Improving Neural Entity Disambiguation with Graph Embeddings (P19-2)
Copied to clipboard
| Challenge: | Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. |
| Approach: | They propose a method that integrates structured information from the knowledge base with unstructured information from text-based representations. |
| Outcome: | The proposed method improves on a graph of hyperlinks between Wikipedia articles and a state-of-the-art neural ED model. |
Entity Linking in the Job Market Domain (2024.findings-eacl)
Copied to clipboard
| Challenge: | In Natural Language Processing, entity linking (EL) has centered around Wikipedia, but yet remains underexplored for the job market domain. |
| Approach: | They propose to use a bi-encoder and an autoregressive model to link fine-grained span-level skill mentions to a specific taxonomy entry to quantify labor market demands. |
| Outcome: | The proposed model outperforms GENRE in strict evaluation, but performs better in loose evaluation. |
BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations (2022.naacl-industry)
Copied to clipboard
Md Tahmid Rahman Laskar, Cheng Chen, Aliaksandr Martsinovich, Jonathan Johnston, Xue-Yong Fu, Shashi Bhushan Tn, Simon Corston-Oliver
| Challenge: | Existing systems that align textual mentions of entities to knowledge bases are difficult to deploy in production environments. |
| Approach: | They propose a neural entity linking system that connects entities in business phone conversations to their corresponding Wikipedia and Wikidata entries. |
| Outcome: | The proposed system improves inference speed and memory consumption while maintaining high accuracy. |
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on Wikipedia-derived KBs, but there is little work on EL over Wikidata . EL systems have found applications in many tasks such as question answering . |
| Approach: | They propose a novel approach to linking entity mentions to referent entities in a knowledge base . they use a sequence-to-sequence model to generate the profile of the target entity . |
| Outcome: | The proposed approach achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010. |
From Zero to Hero: Human-In-The-Loop Entity Linking in Low Resource Domains (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to disambiguate entity mentions in a text depend on training data. |
| Approach: | They propose a domain-agnostic approach to annotate entities using a KB-based approach. |
| Outcome: | The proposed approach outperforms existing methods in a simulation on difficult texts. |