20 citations · 78 across the 19 of their papers we have counts for
4 papers · 1 filter
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder
Minghua Zhang, Yunfang Wu, Weikang Li +1
In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max atten…
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
Wei Li, Xuancheng Ren, Damai Dai +3
Huge numbers of new words emerge every day, leading to a great need for representing them with semantic meaning that is understandable to NLP systems. Sememes are defined as the mi…
An Unsupervised Model with Attention Autoencoders for Question Retrieval
Minghua Zhang, Yunfang Wu
Question retrieval is a crucial subtask for community question answering. Previous research focus on supervised models which depend heavily on training data and manual feature engi…
Improving Word Vector with Prior Knowledge in Semantic Dictionary
Wei Li, Yunfang Wu, Xueqiang Lv
Using low dimensional vector space to represent words has been very effective in many NLP tasks. However, it doesn't work well when faced with the problem of rare and unseen words.…