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Found 12 papers, 6 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.
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
When LLMs Meet Cybersecurity: A Systematic Literature Review
The rapid development of large language models (LLMs) has opened new avenues across various fields, including cybersecurity, which faces an evolving threat landscape and demand for innovative technologies.
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning
In the training process, we train and get the best entity-span detection model and the entity classification model separately on the source domain using meta-learning, where we create a contrastive learning module to enhance entity representations for entity classification.
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.
Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence
Extracting cybersecurity entities such as attackers and vulnerabilities from unstructured network texts is an important part of security analysis.
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
