Papers by Johannes Hoffart
A Study of the Importance of External Knowledge in the Named Entity Recognition Task (P18-2)
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| Challenge: | Existing studies have shown that external knowledge is important for Named Entity Recognition . |
| Approach: | They propose a modular framework that divides knowledge into four categories according to depth . they show the effects when incrementally adding deeper knowledge . |
| Outcome: | The proposed framework outperforms agnostic frameworks with more external knowledge . the proposed frameworks outperformed agrarian frameworks on two standard 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. |
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction (2021.findings-acl)
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Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang’, Johannes Hoffart, Saeedeh Shekarpour, Vijay Saraswat
| Challenge: | Existing methods for relation extraction (RE) use only expanded facts from the knowledge graph . |
| Approach: | They propose a method for relation extraction from a single sentence . they use a neural network to expand the context with additional facts from the KG . |
| Outcome: | The proposed method is more accurate than state-of-the-art methods on standard datasets. |
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata (2021.eacl-main)
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Manoj Prabhakar Kannan Ravi, Kuldeep Singh, Isaiah Onando Mulang’, Saeedeh Shekarpour, Johannes Hoffart, Jens Lehmann
| Challenge: | Existing approaches to target end-to-end entity linking over knowledge bases are not efficient. |
| Approach: | They propose a modular approach to target end-to-end entity linking over knowledge bases. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on two well-known knowledge bases. |
Unsupervised Multi-View Post-OCR Error Correction With Language Models (2021.emnlp-main)
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| Challenge: | Prior work used text generation techniques or redundancy in similar passages for OCR error correction, which is not appropriate in cases of low corpus redundancies or weak document contextual information. |
| Approach: | They propose to use a pretrained language model to reconcile different OCR views in unsupervised way so that their combination contains fewer errors than each individual view. |
| Outcome: | The proposed model can reconcile multiple OCR views so that their combined version contains fewer errors than the best OCR view. |