CUIS Team for NTCIR-13 AKG Task
@inproceedings{Lin2017CUISTF, title={CUIS Team for NTCIR-13 AKG Task}, author={Xinshi Lin and Wai Lam and Shubham Sharma}, booktitle={NTCIR}, year={2017} }
This paper describes our approach for Actionable Knowledge Graph (AKG) task at NTCIR-13. Our ranking system scores each candidate property by combining semantic relevance to action and its document relevance in related entity text descriptions via a Dirichlet smoothing based language model. We employ supervised learning technique to improve performance by minimizing a simple position-sensitive loss function on our additional manually annotated training data from the dry run topics. Our best…
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Overview of NTCIR-13 Actionable Knowledge Graph (AKG) Task
- Computer ScienceNTCIR
- 2017
This paper overviews NTCIR-13 Actionable Knowledge Graph (AKG) task. The task focuses on finding possible actions related to input entities and the relevant properties of such actions. AKG is…
CrossBERT: A Triplet Neural Architecture for Ranking Entity Properties
- Computer ScienceSIGIR
- 2020
This work proposes a new method for property ranking, CrossBERT, which builds on the Bidirectional Encoder Representations from Transformers (BERT) and creates a new triplet network structure on cross query-property pairs that is used to rank properties.
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