To increase the scale and coverage of KGs, a possible solution is to incorporate data from other KGs, and entity alignment (EA) plays a vital role during this process. EA is the task of detecting the entities that refer to the same real-world object but come from different KGs. Although...
Data curation at scale The potential of a single LLM achieving exceptional outcomes across diverse tasks is due to training on an immense volume of Internet-scale data. NVIDIA NeMo Data Curator facilitates the handling of trillion-token multilingual training data for LLMs. It consists ...
We also fully implemented the plans set forth at the Central Conference on Economic Work and in the Report on the Work of the Government approved at the Fifth Session of the 13th National People’s Congress (NPC), and made coordinated advances in all fields of economic and social development....
(2007). Entity extraction is a boring solved problem—or is it? In Human language technologies 2007: The conference of the North American chapter of the Association for Computational Linguistics; Companion Volume, Short Papers (pp. 181–184). Wang, S., & Manning, C.D. (2012). Baselines ...
Cisco Data Intelligence Platform (CDIP) is a cloud-scale architecture, primarily for a private cloud data lake which brings together big data, AI/compute farm, and storage tiers to work together as a single entity while also being able to scale independently to address the IT issues in the ...
Several groups of experiments are set up in each link of construction entity annotation corpus. These experimental data have proven that the proposed method is feasible. 展开 关键词: user dictionary entity corpus conditional random fields constrained conditional random fields ...
Cognitive Graph for Multi-Hop Reading Comprehension at Scale Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, Jie Tang ACL 2019 Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model Wei Li, Jingjing Xu, Yancheng He, Shengli Yan, Yunfang Wu, Xu Sun ACL 2019 Matc...
In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot ...
In the rapidly evolving landscape of financial services, embracing AI and digital innovation at scale has become imperative for banks to stay competitive. With the power of AI and machine learning, financial institutions can leverage predictive analytics, anomaly detection and shared learning models t...