Xin Wang
Harbin Institute of Technology
9 Papers
16 Citations
Xin Wang is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Document clustering & Word (computer architecture). The author has an hindex of 5, co-authored 9 publications.
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Papers
Modelling context with neural networks for recommending idioms in essay writing
TL;DR: A neural network-based approach to address the novel task of recommending idioms in essay writing by encoding semantic representations of variable-sized contexts by considering global and local contexts and modelling them with different schemes.
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Group Linguistic Bias Aware Neural Response Generation
Jianan Wang,Xin Wang,Fang Li,Zhen Xu,Zhuoran Wang,Baoxun Wang +5 more
- 01 Dec 2017
TL;DR: By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented.
13
Extended Dependency-Based Word Embeddings for Aspect Extraction
Xin Wang,Yuanchao Liu,Chengjie Sun,Ming Liu,Xiaolong Wang +4 more
- 16 Oct 2016
TL;DR: This paper introduces outer product of dependency-based word vectors and specialized features as representation of words and shows that it is an effective way to achieve better extraction performance by improving word representations.
11
Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.
Yuanchao Liu,Ming Liu,Xin Wang +2 more
TL;DR: An extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method, and the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly.
Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction
TL;DR: Long Short-Term Memory recurrent neural networks are adopted to address the task of opinion expression extraction and explore the internal mechanisms of the model to provide a novel micro perspective to analyze the run-time processes and gain new insights into the advantages of LSTM selecting the source of information with its flexible connections and multiplier gating operations.
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