HFMRE: Constructing Huffman Tree in Bags to Find Excellent Instances for Distantly Supervised Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to extract sentence-level features are labor-intensive and time-consuming. |
| Approach: | They propose a distantly supervised relation extraction algorithm that uses circular cosine similarity to show intrinsic associations between sentences within a bag. |
| Outcome: | The proposed method outperforms baselines on the popular DSRE datasets. |
Similar Papers
Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Distantly supervised relation extraction is used in knowledge bases but its low quality and noisy sentences are present in sentence bags. |
| Approach: | They propose a multi-layer revision network which emphasizes inner-sentence correlations before extracting relevant information within sentences. |
| Outcome: | The proposed method improves on two New York Times datasets. |
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (N19-1)
Copied to clipboard
| Challenge: | Existing methods to extract relational data generated by distant supervision generate noisy training data. |
| Approach: | They propose a neural relation extraction method to deal with noisy training data generated by distant supervision. |
| Outcome: | Experimental results show that the proposed method is more accurate than state-of-the-art methods on the New York Times dataset. |
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches of distantly supervised relation extraction (DSRE) focus on sentence-level or bag-level de-noising, neglecting the explicit interaction with cross levels. |
| Approach: | They propose a hierarchical contrastive learning framework for distantly supervised relation extraction to reduce noisy sentences. |
| Outcome: | The proposed framework outperforms baselines in various mainstream DSRE datasets. |
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods to find relational facts from texts lack hierarchical information of relations. |
| Approach: | They propose a hierarchical classification framework which extracts relation in a top-down manner. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora . |
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to reduce noise from DS generated training data are not effective for distantly supervised relation extraction (DSRE) |
| Approach: | They propose a multi-instance learning framework to reduce DS noise by dividing training instances into several bags and using them as new data units. |
| Outcome: | The proposed framework improves on NYT10, GDS and KBP with significant improvements over existing methods. |
Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Distantly supervised relation extraction (DSRE) methods are not capable of extracting relation labels for individual sentences. |
| Approach: | They propose a semi-supervised learning relation extraction framework for sentence-level DSRE . they discard only the labels of the noisy samples and utilize them as unlabeled samples . |
| Outcome: | The proposed framework achieves significant performance enhancements on two real-world datasets. |
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning (D18-1)
Copied to clipboard
| Challenge: | Existing methods for extracting relations are slow and lack precision . a novel approach to extract relations is proposed to reduce noise between sentences . |
| Approach: | They propose a word-level distant supervised approach for relation extraction using New York Times and Freebase. |
| Outcome: | The proposed method improves the area of precision/call(PR) from 0.35 to 0.39 over the state-of-the-art methods. |
Combining Distantly Supervised Models with In Context Learning for Monolingual and Cross-Lingual Relation Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Existing Distantly Supervised Relation Extraction models rely on task-specific training, but their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. |
| Approach: | They propose a framework for distantly supervised relation extraction that uses a trained DSRE model to identify the top-k candidate relations for a given test sentence and a dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data. |
| Outcome: | The proposed framework achieves 20 F1 points gains in English and 17 F1 point gains on Indic languages over previous models and naive prompting baselines. |
Denoising Relation Extraction from Document-level Distant Supervision (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to generate auto-labeled sentences for relation extraction (RE) are difficult to extend to document-level relation extraction as noise from DS may be even multiplied in documents. |
| Approach: | They propose a pre-trained model which de-emphasizes noisy DS data via multiple pre-training tasks. |
| Outcome: | The proposed model can capture useful information from noisy data and achieve promising results on the large-scale DocRE benchmark. |
PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extraction (2022.acl-short)
Copied to clipboard
| Challenge: | Recent approaches to distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately. |
| Approach: | They propose a simple baseline approach where sentences of a bag are concatenated into a passage of sentences and encoded jointly using BERT. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and multilingual datasets. |