activity
20182020
most citedSemantic Hilbert Space for Text Representation Learning

47 citations · 96 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG20201 cited

Assessing the Memory Ability of Recurrent Neural Networks

Cheng Zhang, Qiuchi Li, Lingyu Hua +1

It is known that Recurrent Neural Networks (RNNs) can remember, in their hidden layers, part of the semantic information expressed by a sequence (e.g., a sentence) that is being pr…

cs.CL201915 cited

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

Chen Zhang, Qiuchi Li, Dawei Song

Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspec…

cs.CL20192 cited

Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network

Chen Zhang, Qiuchi Li, Dawei Song

It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification. How…

cs.CL201930 cited

CNM: An Interpretable Complex-valued Network for Matching

Qiuchi Li, Benyou Wang, Massimo Melucci

This paper seeks to model human language by the mathematical framework of quantum physics. With the well-designed mathematical formulations in quantum physics, this framework unifi…

cs.CL201947 cited

Semantic Hilbert Space for Text Representation Learning

Benyou Wang, Qiuchi Li, Massimo Melucci +1

Capturing the meaning of sentences has long been a challenging task. Current models tend to apply linear combinations of word features to conduct semantic composition for bigger-gr…

cs.SE20181 cited

Structured Information Retrieval Strategies for Localising Software Changes

Qiuchi Li, Yijun Yu, Dawei Song +1

During software maintenance and evolution, developers need to deal with a large number of change requests by modifying existing code or adding code into the system. An efficient ta…