| Challenge: | a number of documents provide evidence of previous incidents and mitigation strategies . but information about previous projects with similar attributes is often hidden within . a new named entity annotation scheme is being developed for construction safety . |
| Approach: | a team of four health and safety experts have developed a named entity annotation scheme for construction safety documents. |
| Outcome: | a new named entity annotation scheme annotates 600 sentences from accident reports . the scheme has an average agreement rate of 0.79 F-Score . |
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Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)
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| 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. |
UkraiNER: A New Corpus and Annotation Scheme towards Comprehensive Entity Recognition (2024.lrec-main)
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| Challenge: | Named entity recognition excludes nested, discontinuous, non-named entities in practice . despite attempts to broaden their coverage, the most restrictive variant of NER remains the default . |
| Approach: | They propose a new annotation scheme that offers higher comprehensiveness while preserving simplicity. |
| Outcome: | The proposed scheme offers higher comprehensiveness while preserving simplicity . it also includes an annotation tool to implement the scheme on the corpus UkraiNER . |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)
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| Challenge: | Existing NER benchmarks lack quality annotations, resulting in poor performance. |
| Approach: | They propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence. |
| Outcome: | The proposed approach improves NER performance on three datasets with a high number of missing annotations. |
Creating a Dataset for Named Entity Recognition in the Archaeology Domain (2020.lrec-1)
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| Challenge: | Currently, there is no way to find 'by-catch', single finds of a different type, in the metadata of excavation reports. |
| Approach: | They propose to train NER classifiers on Dutch excavation reports to help archaeologists find structured information in archaic documents. |
| Outcome: | The proposed dataset contains 31k annotations between six entity types (artefact, time period, place, context, species & material). |
CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset (2023.emnlp-main)
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| Challenge: | Existing models achieve F1-scores comparable to or exceed noise level in CoNLL-03 . current models have significant annotation errors, incompleteness, and inconsistencies in the data . |
| Approach: | They propose to add a layer of entity linking annotation to the CoNLL-03 corpus to correct 7.0% of all labels. |
| Outcome: | The proposed approach corrects 7.0% of all labels in the English CoNLL-03 dataset. |
KCAT: A Knowledge-Constraint Typing Annotation Tool (P19-3)
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Sheng Lin, Luye Zheng, Bo Chen, Siliang Tang, Zhigang Chen, Guoping Hu, Yueting Zhuang, Fei Wu, Xiang Ren
| Challenge: | Recent years Natural Language Processing community has seen a surge of interest in fine-grained entity typing (FET) given an entity mention (i.e. a sequence of token spans representing an entity), FET aims at uncovering its contextdependent type. |
| Approach: | They propose an efficient Knowledge Constraint Fine-grained Entity Typing Annotation Tool which further improves the entity typing process through entity linking together with some practical functions. |
| Outcome: | The proposed tool improves the entity typing process by linking the candidate types with some practical functions. |
Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases (2020.lrec-1)
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| Challenge: | Current annotation policies for medical corpora are not standardized across clinical texts of different types. |
| Approach: | They propose to annotate medical records of various types using a named entity recognition (NER) task. |
| Outcome: | The proposed annotation scheme is applicable to large-scale clinical NLP projects. |
Comparing Annotated Datasets for Named Entity Recognition in English Literature (2022.lrec-1)
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| Challenge: | Generally speaking, the majority of NER tools struggle to perform well when the entities in the text contain specific characteristics. |
| Approach: | They analysed two existing annotated datasets and two additional gold standard datasets to evaluate the performance of two NER tools. |
| Outcome: | The results show that the performance of two NER tools varies significantly depending on the gold standard used for the individual evaluations. |
Named Entities in Medical Case Reports: Corpus and Experiments (2020.lrec-1)
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| Challenge: | Only very few annotated corpora in the medical domain exist. |
| Approach: | They propose to annotate medical entities in case reports from PubMed Central's open access library. |
| Outcome: | The proposed corpus is the first of its kind to be made available to the scientific community in English. |