no code implementations • 15 May 2023 • Peipei Liu, Hong Li, Yimo Ren, Jie Liu, Shuaizong Si, Hongsong Zhu, Limin Sun
Mining structured knowledge from tweets using named entity recognition (NER) can be beneficial for many down stream applications such as recommendation and intention understanding.
no code implementations • 28 Oct 2022 • Peipei Liu, Xin Zheng, Hong Li, Jie Liu, Yimo Ren, Hongsong Zhu, Limin Sun
At the second stage, a self-supervised contrastive learning is designed for the improvement of the distilled unimodal representations after cross-modal interaction.
no code implementations • 19 Oct 2022 • Peipei Liu, Hong Li, Zhiyu Wang, Yimo Ren, Jie Liu, Fei Lyu, Hongsong Zhu, Limin Sun
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.
1 code implementation • 19 Oct 2022 • Peipei Liu, Gaosheng Wang, Hong Li, Jie Liu, Yimo Ren, Hongsong Zhu, Limin Sun
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.
1 code implementation • 1 Jul 2022 • Peipei Liu, Hong Li, Zuoguang Wang, Jie Liu, Yimo Ren, Hongsong Zhu
Extracting cybersecurity entities such as attackers and vulnerabilities from unstructured network texts is an important part of security analysis.
no code implementations • 15 Mar 2022 • Zuoguang Wang, Yimo Ren, Hongsong Zhu, Limin Sun
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.