| Challenge: | Entity linking (EL) aims to link ambiguous mentions to their corresponding entities in a knowledge base. |
| Approach: | They propose an ELECTRA-based keyword extractor to denoise the mention context and construct a better query expression. |
| Outcome: | The proposed method outperforms state-of-the-art models on the ZESHEL dataset by a significant margin. |
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Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
| Approach: | They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label. |
| Outcome: | The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task. |
Scalable Zero-shot Entity Linking with Dense Entity Retrieval (2020.emnlp-main)
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| Challenge: | Existing methods for entity linking use manually curated mention tables and incoming Wikipedia link popularity. |
| Approach: | They propose a BERT-based entity linking model with a bi-encoder that embeds the mention context and the entity descriptions and then re-ranked the candidate with . they also evaluate the accuracy-speed trade-off inherent to large pre-trained models. |
| Outcome: | The proposed model is state-of-the-art on recent zero-shot benchmarks and established non-zero-shot evaluations. |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
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| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
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. |
Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach (2020.lrec-1)
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| Challenge: | Existing methods for event extraction are "one size fits all" and are not adaptable to new event types or domains of interest. |
| Approach: | They propose a few-shot Event Mention Retrieval task to retrieve event mentions from text . they use existing event datasets such as ACE and a Siamese Network approach . |
| Outcome: | The proposed approach lowers the bar of specifying event-centric information needs. |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
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| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Outcome: | The proposed approach outperforms all other systems on two multilingual benchmarks and is simpler to train and interpretable. |
Label Semantics for Few Shot Named Entity Recognition (2022.findings-acl)
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Jie Ma, Miguel Ballesteros, Srikanth Doss, Rishita Anubhai, Sunil Mallya, Yaser Al-Onaizan, Dan Roth
| Challenge: | Named entity recognition (NER) is a fundamental natural language understanding task that requires large amounts of high quality annotated in-domain data. |
| Approach: | They propose a neural architecture that leverages the semantic information in the names of the labels to give the model additional signal and enriched priors. |
| Outcome: | The proposed model is especially effective in low resource settings. |
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features (2023.findings-emnlp)
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| Challenge: | Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. |
| Approach: | They propose a framework to enable generalization to an unseen target domain with only a few labeled examples. |
| Outcome: | The proposed framework achieves significant performance improvements on in-domain and cross-domain datasets. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |