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Found 6 papers, 6 papers with code
Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks
We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem.
Sampling-based Pseudo-Likelihood for Membership Inference Attacks
In this study, we propose a Sampling-based Pseudo-Likelihood (\textbf{SPL}) method for MIA (\textbf{SaMIA}) that calculates SPL using only the text generated by an LLM to detect leaks.
Generative Data Augmentation for Aspect Sentiment Quad Prediction
Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ASQP
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction
First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence.
Ranked #1 on
Relation Extraction
on ReDocRED
Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized Representations
In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented.
Ranked #3 on
Relation Extraction
on CoNLL04
(NER Micro F1 metric)
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.
