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Found 9 papers, 4 papers with code
Maximal Clique Based Non-Autoregressive Open Information Extraction
However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.
Hierarchical Aligned Multimodal Learning for NER on Tweet Posts
Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many down stream applications such as recommendation and intention understanding.
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis
At the second stage, a self-supervised contrastive learning is designed for the improvement of the distilled unimodal representations after cross-modal interaction.
CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises
Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security.
Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media
With social media posts tending to be multimodal, Multimodal Named Entity Recognition (MNER) for the text with its accompanying image is attracting more and more attention since some textual components can only be understood in combination with visual information.
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems.
Threat Detection for General Social Engineering Attack Using Machine Learning Techniques
This paper explores the threat detection for general Social Engineering (SE) attack using Machine Learning (ML) techniques, rather than focusing on or limited to a specific SE attack type, e. g. email phishing.
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous.
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.
Ranked #2 on
Relation Extraction
on NYT11-HRL
