G P Shrivatsa Bhargav, Dinesh Khandelwal, Saswati Dana, Dinesh Garg, Pavan Kapanipathi, Salim Roukos, Alexander Gray, L Venkata Subramaniam
| Challenge: | Entity linking maps an entity mention in a natural language sentence to an entity in KB. |
| Approach: | They propose a neuro-symbolic, multi-task learning approach to bridge this gap by exploiting an auxiliary information about entity types. |
| Outcome: | The proposed approach achieves significantly higher performance on four different benchmark datasets when trained with just 0.01%, 0.1%, or 1% of the training data. |
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Zero-Shot Entity Linking by Reading Entity Descriptions (P19-1)
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| Challenge: | Existing approaches to link entities to unseen entities require in-domain labeled data. |
| Approach: | They propose a zero-shot entity linking task where mentions must be linked to unseen entities without in-domain labeled data. |
| Outcome: | The proposed task can generalize to unseen entities without metadata or alias tables . the proposed system improves over baselines, including BERT, on a new dataset . |
A Read-and-Select Framework for Zero-shot Entity Linking (2023.findings-emnlp)
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| Challenge: | Existing methods focus on the candidate retrieval stage and ignore the essential candidate ranking stage, which disambiguates among entities and makes the final linking prediction. |
| Approach: | They propose a read-and-select framework that models the main components of entity disambiguation . they use mention context to output mention-aware entity representations . |
| Outcome: | The proposed framework achieves state-of-the-art performance on established zero-shot entity linking dataset ZESHEL with 2.55% micro-average accuracy gain, with no need for laborious multi-phase pre-training used in most of the previous work. |
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. |
Selecting Key Views for Zero-Shot Entity Linking (2023.findings-emnlp)
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| Challenge: | Entity linking is a task of assigning ambiguous mentions in textual input to entities in knowledge bases. |
| Approach: | They propose a framework to align mentions in text to entities in knowledge bases . they use unsupervised clustering to select key views from descriptions . |
| Outcome: | The proposed framework achieves state-of-the-art on the zero-shot entity linking dataset. |
Zero-shot Entity Linking with Efficient Long Range Sequence Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to expand the long-range sequence modeling require expensive pre-training. |
| Approach: | They propose a method to expand the long-range sequence modeling without retraining the BERT model. |
| Outcome: | The proposed method improves the STOA on the zero-shot entity linking dataset by 76.06% and for long data by 74.57%. |
Leveraging Type Descriptions for Zero-shot Named Entity Recognition and Classification (2021.acl-long)
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| Challenge: | Named entity recognition and classification (NERC) tasks require annotated data for the target classes during training. |
| Approach: | They propose a novel approach that leverages the fact that textual descriptions for many entity classes occur naturally. |
| Outcome: | The proposed approach outperforms baselines adapted from machine reading comprehension and zero-shot text classification. |
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
Real World Conversational Entity Linking Requires More Than Zero-Shots (2024.findings-acl)
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| Challenge: | Entity linking (EL) in conversations is a key component of many downstream tasks such as semantic search. |
| Approach: | They propose to use Fandom and Wikipedia to evaluate EL models' ability to generalize to a new unfamiliar KB without prior training. |
| Outcome: | The proposed evaluation framework and dataset are tailored to facilitate the study. |
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. |
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction (2023.acl-demo)
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Gabriele Picco, Marcos Martinez Galindo, Alberto Purpura, Leopold Fuchs, Vanessa Lopez, Thanh Lam Hoang
| Challenge: | ZSL is a machine learning field that uses textual descriptions of entities or relations to perform tasks that are not seen during training. |
| Approach: | They propose a framework that allows researchers to compare state-of-the-art ZSL methods with standard benchmark datasets. |
| Outcome: | The proposed framework compares state-of-the-art methods with benchmark datasets and provides APIs for production under the standard SpaCy NLP pipeline. |