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Found 13 papers, 8 papers with code
MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification
The central problem of sentence classification is to extract multi-scale n-gram features for understanding the semantic meaning of sentences.
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning
Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists.
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
In this paper, we propose two model editing studies and validate them in the medical domain: (1) directly editing the factual medical knowledge and (2) editing the explanations to facts.
Biomedical Entity Linking as Multiple Choice Question Answering
Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities.
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models.
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways.
Ranked #1 on
Link Property Prediction
on ogbl-biokg
Perturbation-based Self-supervised Attention for Attention Bias in Text Classification
In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn.
Multi-modal Contrastive Representation Learning for Entity Alignment
Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities.
Ranked #2 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification
We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification
Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities.
