| Challenge: | Existing bootstrapping methods for Entity Set Expansion suffer from two problems: 1) delayed feedback and sparse supervision. |
| Approach: | They propose a method that estimates delayed feedback and adaptively scores entities given sparse supervision signals. |
| Outcome: | The proposed method can estimate delayed feedback for pattern evaluation and adaptively score entities given sparse supervision signals. |
Similar Papers
Global Bootstrapping Neural Network for Entity Set Expansion (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that end-to-end bootstrapping methods only leverage local semantics rather than global semantics. |
| Approach: | They propose a global-sighted encoder to capture and encode local and global semantics into entity embedding and an attention-guided decoder to sequentially expand new entities based on these embeddables. |
| Outcome: | The proposed network achieves state-of-the-art on two bootstrapping datasets. |
Progressive Adversarial Learning for Bootstrapping: A Case Study on Entity Set Expansion (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for entity set expansion define the expansion boundary using seed-based distance metrics, which are hard to adjust due to the extremely sparse supervision. |
| Approach: | They propose a new learning method for bootstrapping which jointly models the bootstraping process and boundary learning process in a GAN framework. |
| Outcome: | The proposed method achieves the new state-of-the-art performance for entity set expansion. |
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing benchmarks for entity set expansion (ESE) are limited to well-formed text and well-defined concepts. |
| Approach: | They propose to use user-generated text to assess the generalizability of ESE methods by identifying phenomena such as non-named entities, multifaceted entities and vague concepts. |
| Outcome: | The proposed methods are based on user-generated text to assess their generalizability and performance. |
A Practical Incremental Learning Framework For Sparse Entity Extraction (C18-1)
Copied to clipboard
| Challenge: | Existing approaches to extract entities from textual data are expensive and unattractive due to the high cost of training. |
| Approach: | They propose a framework that integrates Entity Set Expansion and Active Learning to reduce the cost of data annotation. |
| Outcome: | The proposed framework reduces the cost of sparse entity annotation by 85% and 45% while maintaining high accuracy. |
Empower Entity Set Expansion via Language Model Probing (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for expanding seed entities with new entities belong to the same semantic class are difficult to implement and can lead to accumulative errors. |
| Approach: | They propose an iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. |
| Outcome: | The proposed framework generates high-quality class names and outperforms state-of-the-art methods significantly. |
Joint Bootstrapping Machines for High Confidence Relation Extraction (N18-1)
Copied to clipboard
| Challenge: | Existing semi-supervised bootstrapping methods for relationship extraction lack labeled data. |
| Approach: | They propose a semi-supervised bootstrapping method that protects against semantic drift . they expand entities and templates in parallel and in mutually constraining fashion in each iteration . |
| Outcome: | Experimental results show that BREX improves on state-of-the-art methods for four relationships. |
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies view entity set expansion, taxonomy expansion, and seed-guided taxonomies as three separate tasks. |
| Approach: | They propose a taxonomy-guided instruction tuning framework to teach a large language model to generate siblings and parents for query entities. |
| Outcome: | The proposed framework outperforms baselines on multiple benchmark datasets. |
Visual Supervision in Bootstrapped Information Extraction (D18-1)
Copied to clipboard
| Challenge: | a list-based interface populated with informative samples is effective for data annotation . a 2D scatterplot populated by diverse and representative samples yields improved models . |
| Approach: | They propose a list-based interface that can be used to build efficient and effective data annotation models. |
| Outcome: | The proposed model learns the distributional similarity of entities through the patterns that match them in a large corpus while being discriminative with respect to human-labeled and machine-promoted entities. |
ExtEnD: Extractive Entity Disambiguation (2022.acl-long)
Copied to clipboard
| Challenge: | Entity disambiguation (ED) is a task in natural language processing that requires a large pre-trained language model to perform. |
| Approach: | They propose a local formulation for Entity Disambiguation (ED) that frames this task as a text extraction problem and propose two Transformer-based architectures that implement it. |
| Outcome: | The proposed model outperforms all its competitors in terms of data efficiency and raw performance on 4 out of 4 benchmarks. |
Weakly Supervised Named Entity Tagging with Learnable Logical Rules (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for building entity tagging systems use weak supervision . previous methods focus on disambiguating entity types based on contexts and expert-provided rules . |
| Approach: | They propose a method that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. |
| Outcome: | The proposed method outperforms weakly supervised methods on three datasets . it rivals state-of-the-art supervised method with lexicon of over 2,000 terms . |