Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval

• URL: http://arxiv.org/abs/2301.13318v1
• Date: Mon, 30 Jan 2023 22:43:21 GMT
• Title: Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval
• Authors: Maciej Wiatrak, Eirini Arvaniti, Angus Brayne, Jonas Vetterle, Aaron Sim
• Abstract summary: We show that pairing a proxy-based metric learning loss with an adversarial regularizer provides an efficient alternative to hard negative sampling in the candidate retrieval stage. In particular, we show competitive performance on the recall@1 metric, thereby providing the option to leave out the expensive candidate ranking step.
• Score: 3.1498833540989413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms, an initial candidate retrieval stage that generates a shortlist of entities for each mention, followed by a candidate ranking stage. However, the effectiveness of both stages are inextricably dependent on computationally expensive components. Specifically, in candidate retrieval via dense representation retrieval it is important to have hard negative samples, which require repeated forward passes and nearest neighbour searches across the entire entity label set throughout training. In this work, we show that pairing a proxy-based metric learning loss with an adversarial regularizer provides an efficient alternative to hard negative sampling in the candidate retrieval stage. In particular, we show competitive performance on the recall@1 metric, thereby providing the option to leave out the expensive candidate ranking step. Finally, we demonstrate how the model can be used in a zero-shot setting to discover out of knowledge base biomedical entities.

Related papers

• Unified Examination of Entity Linking in Absence of Candidate Sets [3.55026004901472]
  We use an ablation study to investigate the impact of candidate sets on the performance of entity linking. We show the trade-off between less restrictive candidate sets, increased inference time and memory footprint for some models.
  arXiv  Detail & Related papers  (2024-04-17T04:37:58Z)
• Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors [4.31947784387967]
  In Knowledge Graphs on a larger scale, the ranking process rapidly becomes heavy. Previous approaches used random sampling of entities to assess the quality of links predicted or suggested by a method. We show that this approach has serious limitations since the ranking metrics produced do not properly reflect true outcomes. We propose a framework that uses relational recommenders to guide the selection of candidates for evaluation.
  arXiv  Detail & Related papers  (2024-01-25T15:44:46Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• The Battleship Approach to the Low Resource Entity Matching Problem [0.0]
  We propose a new active learning approach for entity matching problems. We focus on a selection mechanism that exploits unique properties of entity matching. An experimental analysis shows that the proposed algorithm outperforms state-of-the-art active learning solutions to low resource entity matching.
  arXiv  Detail & Related papers  (2023-11-27T10:18:17Z)
• A Read-and-Select Framework for Zero-shot Entity Linking [33.15662306409253]
  We propose a read-and-select (ReS) framework by modeling the main components of entity disambiguation. Our method achieves the state-of-the-art performance on the established zero-shot entity linking dataset ZESHEL with a 2.55% micro-average accuracy gain.
  arXiv  Detail & Related papers  (2023-10-19T04:08:10Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem [46.028180604304985]
  We propose an autoregressive entity linking model, that is trained with two auxiliary tasks, and learns to re-rank generated samples at inference time. We show through ablation studies that each of the two auxiliary tasks increases performance, and that re-ranking is an important factor to the increase.
  arXiv  Detail & Related papers  (2022-04-12T17:55:22Z)
• Diverse Knowledge Distillation for End-to-End Person Search [81.4926655119318]
  Person search aims to localize and identify a specific person from a gallery of images. Recent methods can be categorized into two groups, i.e., two-step and end-to-end approaches. We propose a simple yet strong end-to-end network with diverse knowledge distillation to break the bottleneck.
  arXiv  Detail & Related papers  (2020-12-21T09:04:27Z)
• Minimax Active Learning [61.729667575374606]
  Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. We develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner.
  arXiv  Detail & Related papers  (2020-12-18T19:03:40Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.