Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering (N19-1)
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| Challenge: | Existing studies have addressed this problem with partial-label loss, but they suffer from confirmation bias, which means the classifier fit a pseudo data distribution given by itself. |
| Approach: | They propose to regularize distantly supervised models with Compact Latent Space Clustering to bypass this problem and effectively utilize noisy data yet. |
| Outcome: | The proposed model outperforms state-of-the-art models on standard benchmarks on fine-grained entity typing (FET) by a significant margin. |
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| Challenge: | Fine-grained entity typing (FET) aims to assign semantically rich and contextually appropriate types to entity mentions. |
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| Challenge: | Ultra-fine entity typing is a task of inferring the semantic types from a large set of fine-grained candidates that apply to a given entity mention. |
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| Challenge: | Existing methods for fine-grained type classification rely on distant supervision and are susceptible to noisy labels that can be out-of-context or overly-specific. |
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| Challenge: | Entity typing is the task of assigning semantic types to entities mentioned in text. |
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| Challenge: | Existing approaches to fine-grained entity typing are limited by the errors in the annotation process. |
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Learning to Denoise Distantly-Labeled Data for Entity Typing (N19-1)
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| Challenge: | Distantly-labeled data can be used to scale up statistical models, but it is noisy . specialized probabilistic models can be employed to scale the training of models, however, they require sophisticated probabilistic inference for the training. |
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Fine-grained Entity Typing without Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing work on fine-grained entity typing (FET) relies on knowledge bases as distant supervision, but lack of or incompleteness of KB can hinder training. |
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Improving Fine-grained Entity Typing with Entity Linking (D19-1)
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| Challenge: | Existing methods for fine-grained entity typing require a large tag set and knowledge of the context. |
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Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition (2022.findings-naacl)
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| Challenge: | Existing methods to locate and classify entities using knowledge bases and unlabeled corpus are expensive and limited application. |
| Approach: | They propose to use a method to directly learn the distant label refinement knowledge by imitating annotations of different qualities and comparing them in contrastive learning frameworks. |
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Denoising Enhanced Distantly Supervised Ultrafine Entity Typing (2023.findings-acl)
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| Challenge: | Recent work on distantly supervised (DS) ultra-fine entity typing has received significant attention . however, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. |
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