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Found 9 papers, 3 papers with code
Demonstrating Mutual Reinforcement Effect through Information Flow
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks.
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification
The proliferation of Large Language Models (LLMs) has spurred extensive research into LLM-related Prompt investigations, such as Instruction Learning (IL), In-context Learning (ICL), and Chain-of-Thought (CoT).
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
In this vein, we introduce the General Information Extraction Large Language Model (GIELLM), which integrates text Classification, Sentiment Analysis, Named Entity Recognition, Relation Extraction, and Event Extraction using a uniform input-output schema.
USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset
It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination.
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks
In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks.
Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition
Information extraction(IE) is a crucial subfield within natural language processing.
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks
Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed.
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network
Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures the reliability of WSNs.
