SOTAVerified

Relation Extraction

Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

Source: Deep Residual Learning for Weakly-Supervised Relation Extraction

Papers

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Showing 451–500 of 1977 papers

TitleStatusHype
A Survey on Extraction of Causal Relations from Natural Language Text—0
Creating a Fine Grained Entity Type Taxonomy Using LLMs—0
Creating a Gold Standard Corpus for the Extraction of Chemistry-Disease Relations from Patent Texts—0
Creation of an Annotated Corpus of Spanish Radiology Reports—0
A survey on cutting-edge relation extraction techniques based on language models—0
CREFT: Sequential Multi-Agent LLM for Character Relation Extraction—0
Comparing Encoder-Only and Encoder-Decoder Transformers for Relation Extraction from Biomedical Texts: An Empirical Study on Ten Benchmark Datasets—0
A Decoupling and Aggregating Framework for Joint Extraction of Entities and Relations—0
Cross-Document Temporal Relation Extraction with Temporal Anchoring Events—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Cross-Lingual Adaptation Using Universal Dependencies—0
Cross-lingual Inference with A Chinese Entailment Graph—0
Diversifiable Bootstrapping for Acquiring High-Coverage Paraphrase Resource—0
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models—0
Cross-lingual Structure Transfer for Relation and Event Extraction—0
Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data—0
A Survey of Distant Supervision Methods using PGMs—0
Automatically Labeled Data Generation for Large Scale Event Extraction—0
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas—0
Cross-Sentence N-ary Relation Extraction with Graph LSTMs—0
BERTChem-DDI : Improved Drug-Drug Interaction Prediction from text using Chemical Structure Information—0
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks—0
Comparable Study of Event Extraction in Newswire and Biomedical Domains—0
Event-Arguments Extraction Corpus and Modeling using BERT for Arabic—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
DARE: Data Augmented Relation Extraction with GPT-2—0
Data-driven Paraphrasing and Stylistic Harmonization—0
Dataset for N-ary Relation Extraction of Drug Combinations—0
A Comparison of Weak Supervision methods for Knowledge Base Construction—0
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
Dealing with negative samples with multi-task learning on span-based joint entity-relation extraction—0
Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction—0
Improving Distant Supervised Relation Extraction by Dynamic Neural Network—0
Deep Bidirectional Transformers for Relation Extraction without Supervision—0
COMM:Concentrated Margin Maximization for Robust Document-Level Relation Extraction—0
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification—0
Deep Exhaustive Model for Nested Named Entity Recognition—0
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature—0
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population—0
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text—0
A Study of Recent Contributions on Information Extraction—0
Deep Learning on Graphs for Natural Language Processing—0
Combining Tree Structures, Flat Features and Patterns for Biomedical Relation Extraction—0
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey—0
Combining Temporal Information and Topic Modeling for Cross-Document Event Ordering—0
A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards—0
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels—0
Combining Neural Networks and Log-linear Models to Improve Relation Extraction—0
A Study of Hybrid Similarity Measures for Semantic Relation Extraction—0
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction—0
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All datasetsDocREDTACREDSemEval-2010 Task-8ACE 2005CoNLL04Adverse Drug Events (ADE) CorpusWebNLGChemProtACE 2004NYT11-HRLCDRGDA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DREEAMF167.53—Unverified
2KD-Rb-lF167.28—Unverified
3SSAN-RoBERTa-large+AdaptationF165.92—Unverified
4SAIS-RoBERTa-largeF165.11—Unverified
5Eider-RoBERTa-largeF164.79—Unverified
6DocuNet-RoBERTa-largeF164.55—Unverified
7CGM2IR-RoBERTalargeF163.89—Unverified
8SETE-Roberta-largeF163.74—Unverified
9ATLOP-RoBERTa-largeF163.4—Unverified
10DRE-MIR-BERTbaseF163.15—Unverified
#ModelMetricClaimedVerifiedStatus
1RAG4REF186.6—Unverified
2DeepStruct multi-task w/ finetuneF176.8—Unverified
3UNiST (LARGE)F175.5—Unverified
4RE-MCF175.4—Unverified
5GenPT (T5)F175.3—Unverified
6RECENT+SpanBERTF175.2—Unverified
7SuRE (PEGASUS-large)F175.1—Unverified
8EXOBRAINF175—Unverified
9Relation ReductionF174.8—Unverified
10RoBERTa-large-typed-markerF174.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SPF191.9—Unverified
2RIFREF191.3—Unverified
3REDNF191—Unverified
4SPOTF190.6—Unverified
5KLGF190.5—Unverified
6RELAF190.4—Unverified
7Skeleton-Aware BERTF190.36—Unverified
8LUKEF190.3—Unverified
9KnowPromptF190.3—Unverified
10EPGNNF190.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Span-levelNER Micro F185.98—Unverified
2Dual Pointer Network(multi-head)Relation classification F180.8—Unverified
3Dual Pointer NetworkRelation classification F180.5—Unverified
4PL-MarkerRE Micro F173—Unverified
5ASP+T5-3BRE Micro F172.7—Unverified
6GoLLIERE Micro F170.1—Unverified
7Ours: cross-sentence ALBRE Micro F169.4—Unverified
8MGERE+ Micro F168.2—Unverified
9HySPA (ours) w/ RoBERTaRelation F168.2—Unverified
10RNN+CNNRelation classification F167.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ReLiK-LargeRE+ Micro F178.1—Unverified
2REBELRE+ Macro F1 76.65—Unverified
3ASP+T0-3BRE+ Micro F176.3—Unverified
4Table-SequenceRE+ Macro F1 75.4—Unverified
5SpERTRE+ Macro F1 72.87—Unverified
6DeeperRE+ Macro F1 72.63—Unverified
7TablERTRE+ Micro F172.6—Unverified
8TANLRE+ Micro F172.6—Unverified
9TriMFRE+ Micro F172.35—Unverified
10Multi-turn QARE+ Micro F168.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PFN (ALBERT XXL, average aggregation)RE+ Macro F183.9—Unverified
2DeeperRE+ Macro F183.74—Unverified
3PFN (ALBERT XXL, no aggregation)RE+ Macro F183.2—Unverified
4SpERT.PL (without overlap and BioBERT)RE+ Macro F182.39—Unverified
5REBEL (including overlapping entities)RE+ Macro F182.2—Unverified
6SpERT.PL (with overlap and BioBERT)RE+ Macro F182.03—Unverified
7CMANRE+ Macro F181.14—Unverified
8Table-SequenceRE+ Macro F180.1—Unverified
9CLDR + CLNERRE+ Macro F179.97—Unverified
10SpERT (without overlap)RE+ Macro F179.24—Unverified
#ModelMetricClaimedVerifiedStatus
1UniRelF194.7—Unverified
2PFNF193.6—Unverified
3SPNF193.4—Unverified
4TDEERF193.1—Unverified
5RIFREF192.6—Unverified
6TPLinkerF191.9—Unverified
7HBT (CasRel)F191.8—Unverified
8RIN (BERT, K=2)F190.1—Unverified
9CGT(UniLM)F183.4—Unverified