40 citations · 53 across the 4 of their papers we have counts for
4 papers
Assisted Learning for Organizations with Limited Imbalanced Data
Cheng Chen, Jiaying Zhou, Jie Ding +1
In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained mod…
Improving Entity Linking through Semantic Reinforced Entity Embeddings
Feng Hou, Ruili Wang, Jun He +1
Entity embeddings, which represent different aspects of each entity with a single vector like word embeddings, are a key component of neural entity linking models. Existing entity…
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
Haodi Zhang, Zihang Gao, Yi Zhou +3
Deep reinforcement learning has been successfully used in many dynamic decision making domains, especially those with very large state spaces. However, it is also well-known that d…
KDSL: a Knowledge-Driven Supervised Learning Framework for Word Sense Disambiguation
Shi Yin, Yi Zhou, Chenguang Li +4
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning. First, from WordNet,…