Papers by Shixing Liu

2 papers
Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)

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Challenge: Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs.
Approach: They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks.
Outcome: The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings.
T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine (C18-2)

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Challenge: Traditional Chinese Medicine (TCM) is one of precious intangible cultural heritages of the Chinese nation.
Approach: They propose to use authorized and anonymized clinical records, medicine clinical guidelines, teaching materials, classic medical books, academic publications, etc. as data resources to build a TCM knowledge graph.
Outcome: The proposed system extracts triples from free texts to build a TCM knowledge graph.

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