Papers with Named Entity Disambiguation
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora (P18-2)
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| Challenge: | Named Entity Disambiguation (NED) systems perform well on news articles but quality drops when inputs span long time periods. |
| Approach: | They propose a time-aware method that resolves ambiguities even when mention contexts give only few cues. |
| Outcome: | The proposed method improves on a newly created diachronic corpus. |
Multimodal Knowledge Learning for Named Entity Disambiguation (2022.findings-emnlp)
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| Challenge: | Existing attempts to model multimodal information at the knowledge level are lacking multimodal annotation data against the large-scale unlabeled corpus. |
| Approach: | They propose to use multimodal knowledge learning to link ambiguous mentions with textual and visual contexts to a predefined knowledge graph. |
| Outcome: | The proposed method achieves improvements over the state-of-the-art methods on two public MNED datasets. |
Developing New Linguistic Resources and Tools for the Galician Language (L18-1)
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| Challenge: | Existing resources and tools for the Galician language are lacking for other less-resourced languages, such as statistical tools for lemmatization and Named Entity Recognition. |
| Approach: | They propose to develop a manually revised corpus for POS tagging and lemmatization, and a new manually annotated corpus to train existing statistical tools for the Galician language. |
| Outcome: | The proposed resources include a new corpus for POS tagging and lemmatization, and a manually annotated corpus to handle Named Entity recognition. |
Unsupervised Named Entity Disambiguation for Low Resource Domains (2024.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Disambiguation (NED) are inefficient for domain specific tasks such as searching, question answering and information extraction. |
| Approach: | They propose a unsupervised approach leveraging the concept of Group Steiner Trees which can identify the most relevant candidate for entity disambiguation using contextual similarities across candidate entities for all the mentions present in a document. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods by more than 40% in terms of Precision@1 and Hit@5 across various domain-specific datasets. |