15 citations · 61 across the 12 of their papers we have counts for
7 papers · 1 filter
Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification
Qianwen Ma, Chunyuan Yuan, Wei Zhou +2
Automatically labeling multiple styles for every song is a comprehensive application in all kinds of music websites. Recently, some researches explore review-driven multi-label mus…
Jointly embedding the local and global relations of heterogeneous graph for rumor detection
Chunyuan Yuan, Qianwen Ma, Wei Zhou +2
The development of social media has revolutionized the way people communicate, share information and make decisions, but it also provides an ideal platform for publishing and sprea…
Learning review representations from user and product level information for spam detection
Chunyuan Yuan, Wei Zhou, Qianwen Ma +3
Opinion spam has become a widespread problem in social media, where hired spammers write deceptive reviews to promote or demote products to mislead the consumers for profit or fame…
TransSent: Towards Generation of Structured Sentences with Discourse Marker
Xing Wu, Dongjun Wei, Liangjun Zang +2
Structured sentences are important expressions in human writings and dialogues. Previous works on neural text generation fused semantic and structural information by encoding the e…
"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer
Xing Wu, Tao Zhang, Liangjun Zang +2
This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attr…
Imbalanced Sentiment Classification Enhanced with Discourse Marker
Tao Zhang, Xing Wu, Meng Lin +2
Imbalanced data commonly exists in real world, espacially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data. We ob…
AccUDNN: A GPU Memory Efficient Accelerator for Training Ultra-deep Neural Networks
Jinrong Guo, Wantao Liu, Wang Wang +4
Typically, Ultra-deep neural network(UDNN) tends to yield high-quality model, but its training process is usually resource intensive and time-consuming. Modern GPU's scarce DRAM ca…