A new model improves relation extraction accuracy through relation-gated adversarial learning.
problem Relation extraction from sentences is challenging due to expensive human annotation and noisy distant supervision.
method Proposes relation-gated adversarial learning for relation extraction, extending domain adaptation methods.
result The model outperforms previous domain adaptation methods and improves accuracy of distance supervised relation extraction.
Convolutional neural networks win SemEval-2017 for scientific relation extraction.
problem Extracting relations between scientific concepts from scholarly articles.
method Convolutional neural network model for relation extraction.
result Ranked first in SemEval-2017 Task 10 for relation extraction in scientific articles.
CURE extracts relations without supervision by clustering similar entity pairs.
problem Extracting relations unsupervised without considering sentence correlations.
method CURE uses Encoder-Decoder architecture for self-supervised learning and clustering similar relations.
result CURE outperforms state-of-the-art models on NYT and UNPC datasets.
Extract keyphrases and relations from scientific documents.
problem Understanding which publications describe which processes, tasks, and materials.
method Evaluated 26 submissions across 3 scenarios.
result Task and findings relevant for researchers and information extraction communities.
Paper uses AC-GAN to generate high-quality relational sentences for relation extraction.
problem Limited training data for relation extraction models.
method Auxiliary Classifier Generative Adversarial Networks (AC-GANs).
result Significantly improved performance of relation extraction.
Neural networks enhance relation extraction from biomedical literature.
problem Automated extraction of relations between biomedical concepts.
method Use of multichannel architectures in deep neural networks with biomedical ontologies.
result State-of-the-art results in relation extraction tasks.
Novel unsupervised relation extraction framework using BERT.
problem Relation extraction without supervision.
method Syntactic parsing, pre-trained embeddings, distant supervision, fine-tuning BERT.
result Significantly outperforms baselines and matches state-of-the-art in three out of four data sets.
GenIE extracts structured text with fewer errors and more entities.
problem Closed information extraction with errors and limited entities.
method End-to-end autoregressive generation with bi-level constraints.
result State-of-the-art performance on closed information extraction.
Study evaluates gender bias in relation extraction systems.
problem Gender bias in relation extraction systems.
method Created WikiGenderBias dataset, evaluated systems for bias, analyzed bias mitigation techniques.
result NRE systems exhibit gender bias in predictions.
Paper proposes nearly unsupervised hashcode learning for relation extraction.
problem Relation extraction from text, especially in biomedical domains.
method Optimized hashcode representations learned from data points without class labels, followed by supervised classification.
result Significant accuracy improvements over state-of-the-art methods.
Paper improves relation extraction in clinical texts with limited data.
problem Relation extraction in narrow knowledge domains with scarce annotated data.
method Introduces a bag-of-concepts (BoC) model and compares it with window-bounded co-occurrence (WBC).
result BoC model outperforms baseline and other complex methods on small dataset.
Deep learning model extracts medical treatment-problem relationships.
problem Mining relationships between treatments and medical problems.
method Hybrid approach combining deep learning and rule-based systems.
result System achieved promising performance on medical relation extraction task.
DARE uses GPT-2 to generate relation examples, improving RE performance.
problem Challenges in Relation Extraction, especially limited data and class imbalance.
method Data augmentation with GPT-2 fine-tuning to generate relation examples.
result Improves RE performance by up to 11 F1 score points.
Bayesian meta-learning on relation graphs improves few-shot relation extraction.
problem Predicting relations in sentences with limited labeled examples.
method Bayesian meta-learning on a global relation graph, using graph neural networks and Langevin dynamics.
result Framework effectively learns and generalizes to new relations.
Relational Autoencoder improves feature extraction by considering data relationships.
problem Feature extraction from high-dimensional data fails to consider data relationships.
method Proposes a Relation Autoencoder model that considers both features and relationships.
result Considering data relationships generates more robust features with lower error rates.
Paper proposes a novel model to improve n-ary cross-sentence relation extraction by addressing noisy data and non-consecutive sentences.
problem Noisy labeled data and non-consecutive sentences in n-ary cross-sentence relation extraction.
method Two-level agent reinforcement learning model and hybrid attention mechanism/PCNN approach.
result The model reduces the impact of noisy data and achieves better performance.
This work formalizes and solves GNN extraction, a new type of attack.
problem Extracting the underlying model from a black-box GNN with API access.
method Formalizes GNN extraction, presents a solution with preliminary results.
result Preliminary results show the feasibility of GNN extraction.
Proposes OpenKI for better web-scale knowledge extraction and alignment.
problem Combining OpenIE and KB for web-scale knowledge extraction and alignment.
method Instance-level inference using neighborhood information from KB and OpenIE extractions, with attention mechanisms.
result Significantly improves performance on OpenIE extractions and semi-structured data.
Hi-RES framework extracts medical relations from articles and EHRs.
problem Manual annotation bottleneck in relation extraction.
method Labeling sentences, creating improved negative samples, using pretrained language models, and combining EHR embeddings.
result Significant accuracy increases in relation extraction, up to 0.998 for disorder-location relations.
Efficiently use expert time in relation extraction by using distant supervision and active learning.
problem Scarcity of labeled data in relation extraction, especially with expert time constraints.
method Use distant supervision to label data and active learning to refine results with expert feedback.
result Improved relation extraction results with active learning from expert feedback.
With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedu…
Extracts biological context from biomedical texts to associate with events.
problem Identifying biological context and associating it with biochemical events in texts.
method Analyzed an annotated corpus and developed classifiers using syntactic, distance, and frequency features.
result Developed and evaluated classifiers for context-event association.
This study uses partially annotated data to improve TempRel extraction.
problem Lack of fully annotated data for TempRel extraction makes the task labor-intensive and limited in coverage.
method Utilizes partially annotated data (P) for TempRel extraction, even when annotations are missing.
result Partially annotated data (P) can still be a useful supervision signal for TempRel extraction within a constrained learning framework.
ELICA helps analysts extract relevant information during elicitation meetings.
problem Challenges in eliciting requirements due to analyst's lack of domain knowledge.
method ELICA uses a novel information extraction algorithm combining WFSTs and SVMs, presented in an interactive GUI.
result ELICA effectively extracts relevant information in real-time and facilitates interactive and dynamic process.
This paper extracts topic-specific subgraphs from Wikidata for easier research.
problem Time-consuming and task-specific tuning in using Wikidata for graph processing.
method Unified framework to extract topic-specific subgraphs from Wikidata.
result Standardized sub-graphs for easier evaluation of graph processing algorithms.
The paper introduces a new language for semi-supervised learning tasks.
problem Handling semi-supervised learning with declarative constraints.
method Developed a declarative language for modeling both supervised and semi-supervised learning tasks, including heuristics and combining multiple heuristics.
result Improved performance on relation-extraction tasks for real-world domains.
ExpBERT uses natural language explanations to improve text interpretation.
problem Improving text interpretation for relation extraction tasks.
method Fine-tuning BERT on MultiNLI to interpret natural language explanations.
result ExpBERT matches a BERT baseline but requires less labeled data and improves performance.
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
Method quantifies relation similarity using entity pair distributions.
problem Measuring similarity between relations in knowledge bases.
method Simple neural network parameterizes conditional probability distributions over entity pairs. Sampling-based approximation for similarity computation.
result Approximation correlates with human judgments and detects redundant relations.
AIME embeds multi-omics data to adjust confounders and find related features.
problem Extracting meaningful relationships between complex omics data types while accounting for confounders.
method Autoencoder-based deep learning approach that incorporates clinical confounders.
result AIME effectively adjusts for confounders and extracts biologically relevant features.
SARN improves relational reasoning with less computation.
problem Efficiently perform relational reasoning with reduced computation.
method Introduces SARN, a sequential attention relational network.
result SARN achieves high accuracy on relational questions.
Rk-means clusters relational data without full matrix, speeding up clustering.
problem Clustering relational data without full matrix computation.
method Constructs a grid coreset for clustering, avoiding expensive feature extraction queries.
result Orders-of-magnitude speedup in clustering relational data.
Extracts behavioral features from smartphone and wearable data.
problem Processing raw data streams from smartphones and wearables for human behavior analysis.
method Generic framework for processing raw data streams and extracting behavioral features.
result Extracts useful features related to non-verbal human behavior from raw data streams.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
Paper presents a technique using Spearman's Rank Correlation Coefficient for KE in TDs.
problem Extracting common characteristics and grouping similar TDs.
method Spearman's Rank Correlation Coefficient (SRCC) for KE.
result SRCC proves a comprehensive measure for high-quality KE.
A new framework predicts stock movements using news sentiment and relational data.
problem Predicting stock prices from textual information is challenging due to market uncertainty and natural language complexity.
method Multi-Graph Recurrent Network (MGRN) combining textual sentiment from financial news and relational data.
result The model outperforms benchmarks in predicting stock movements.
Defines cost of MEV and shows its relevance in various settings.
problem Excess value miners can realize by manipulating transaction order.
method Introduces a simple theoretical definition of cost of MEV, proves properties, and provides examples.
result Reveals the cost of MEV is related to the 'smoothness' of a function over the symmetric group.
Neural network feature extraction is compared to statistical physics renormalization group flow.
problem Understanding how deep neural networks extract hierarchical features from input images.
method Used RBM to model Ising model and analyze weight matrices of trained RBM.
result RBM trained on spin configurations flows towards critical temperature Tc instead of typical RG flow. FinCausal 2020 task detects financial document causality.
problem Detect causal relationships in financial documents.
method Binary classification and relation extraction tasks.
result 16 teams participated, 13 submitted system descriptions.
Improved stock prediction using news features and RNN.
problem Predicting stock prices with high accuracy.
method Extracted news features, optimized seed words, calculated positive polar, constructed news features, proposed RNN model.
result Our method improves stock prediction accuracy by over 5%.
Declarative entropy constraints improve semi-supervised learning.
problem Improving semi-supervised learning performance.
method Declarative specification of entropy constraints for semi-supervised learning.
result Consistent improvements on SSL benchmarks, including a new state-of-the-art result.
Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient…
The paper explores the pentagon relation and its algebraic forms.
problem Exploring the pentagon relation and its various forms.
method Starting with geometric form, then algebraic form as a family of equations, deriving equivalent forms using 6j-symbols, and extracting solutions from modular categories.
result Extracting a solution of the pentagon relation from any modular category.
A new method extracts events and their arguments efficiently from text.
problem Efficiently extract event information from texts with long-range dependencies and associations.
method Graph Convolutional Networks with shortest dependency paths to capture syntactic relationships.
result Significant improvement over state-of-the-art methods.
A method reduces dimensionality for multi-block data, enhancing feature extraction and classification accuracy.
problem Tractable feature extraction from large-scale, multi-dimensional data.
method Common and individual feature extraction from multi-block data structures using tensor decompositions.
result Significant reduction in dimensionality and enhanced accuracy in feature extraction and classification.
New algorithms detect clusters in multiplex networks.
problem Detecting clusters in networks with multiple layers.
method Network Fusion for Composite Community Extraction (NF-CCE) using non-negative matrix factorization.
result NF-CCE outperforms state-of-the-art methods on various multiplex networks.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
A new ICA method adds L1-regularization for better interpretability of fMRI data.
problem Improving interpretability of ICA features in high-dimensional fMRI data.
method L1-regularization added to ICA cost function, solved by DCA.
result Validated on synthetic and real fMRI data, improving feature interpretability.