Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling [PDF]
AAAI Conference on Artificial Intelligence, 2020 Document-level relation extraction (RE) poses new challenges compared to its sentence-level counterpart. One document commonly contains multiple entity pairs, and one entity pair occurs multiple times in the document associated with multiple possible ...
Wenxuan Zhou +3 more
semanticscholar +1 more source
Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 The DocRED dataset is one of the most popular and widely used benchmarks for document-level relation extraction (RE). It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset.
Qingyu Tan +4 more
semanticscholar +1 more source
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction [PDF]
Conference on Empirical Methods in Natural Language Processing, 2022 The diverse relationships among real-world events, including coreference, temporal, causal, and subevent relations, are fundamental to understanding natural languages.
Xiaozhi Wang +11 more
semanticscholar +1 more source
Consistent Prototype Learning for Few-Shot Continual Relation Extraction
Annual Meeting of the Association for Computational Linguistics, 2023 Few-shot continual relation extraction aims to continually train a model on incrementally few-shot data to learn new relations while avoiding forgetting old ones. However, current memory-based methods are prone to overfitting memory samples, resulting in
Xiudi Chen, Hui Wu, Xiaodon Shi
semanticscholar +1 more source
REFinD: Relation Extraction Financial Dataset [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 A number of datasets for Relation Extraction (RE) have been created to aide downstream tasks such as information retrieval, semantic search, question answering and textual entailment.
Simerjot Kaur +7 more
semanticscholar +1 more source
BioRel: towards large-scale biomedical relation extraction
BMC Bioinformatics, 2020 Background Although biomedical publications and literature are growing rapidly, there still lacks structured knowledge that can be easily processed by computer programs.
Rui Xing, Jie Luo, Tengwei Song
doaj +1 more source
An Entity Relation Extraction Method Based on Dynamic Context and Multi-Feature Fusion
Applied Sciences, 2022 Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically.
Xiaolin Ma +3 more
doaj +1 more source
Rethinking Multimodal Entity and Relation Extraction from a Translation Point of View
Annual Meeting of the Association for Computational Linguistics, 2023 We revisit the multimodal entity and relation extraction from a translation point of view. Special attention is paid on the misalignment issue in text-image datasets which may mislead the learning.
Changmeng Zheng +4 more
semanticscholar +1 more source
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
Xuming Hu +4 more
semanticscholar +1 more source
An Open Relation Extraction Method for Domain Text Based on Hybrid Supervised Learning
Applied Sciences, 2023 Current research on knowledge graph construction is focused chiefly on general-purpose fields, whereas constructing knowledge graphs in vertically segmented professional fields faces numerous difficulties.
Xiaoxiong Wang, Jianpeng Hu
doaj +1 more source