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Credits

This project uses openly available knowledge graphs, datasets and code. This document acknowledges the resources that were helpful in this project.

Knowledge Graphs

ConceptNet

This work includes data from ConceptNet 5, which was compiled by the Commonsense Computing Initiative. ConceptNet 5 is freely available under the Creative Commons Attribution-ShareAlike license (CC BY SA 4.0) from https://conceptnet.io. The included data was created by contributors to Commonsense Computing projects, contributors to Wikimedia projects, Games with a Purpose, Princeton University's WordNet, DBPedia, OpenCyc, and Umbel.

WordNet

Princeton University "About WordNet." WordNet. Princeton University. 2010.

Datasets

SNIPS

We obtained the train/test set from https://github.com/congyingxia/ZeroShotCapsule/tree/master/data/nlu_data.

Congying Xia*, Chenwei Zhang*, Xiaohui Yan, Yi Chang, Philip S. Yu. Zero-shot User Intent Detection via Capsule Neural Networks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.

Coucke, A., Saade, A., Ball, A., Bluche, T., Caulier, A., Leroy, D., Doumouro, C., Gisselbrecht, T., Caltagirone, F., Lavril, T., Primet, M., & Dureau, J. (2018). Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces. ArXiv, abs/1805.10190.

aPY

We obtained the aPY dataset from https://vision.cs.uiuc.edu/attributes/.

Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009). Describing objects by their attributes. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1778-1785.

AWA2

We obtained the AWA2 dataset from https://cvml.ist.ac.at/AwA2/.

Xian, Y., Lampert, C.H., Schiele, B., & Akata, Z. (2019). Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2251-2265.

ImageNet

We obtained the ImageNet dataset from image-net.org.

Olga Russakovsky*, Jia Deng*, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei. (* = equal contribution) ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015.

BBN and OntoNotes

We obtained the BBN and OntoNotes dataset from https://github.com/INK-USC/AFET.

Ren, X., He, W., Qu, M., Huang, L., Ji, H., & Han, J. (2016). AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding. EMNLP.

Original dataset paper: BBN

Weischedel, Ralph, and Ada Brunstein. BBN Pronoun Coreference and Entity Type Corpus LDC2005T33. Web Download. Philadelphia: Linguistic Data Consortium, 2005

Original dataset paper: OntoNotes

Gillick, D., Lazic, N., Ganchev, K., Kirchner, J., & Huynh, D. (2014). Context-Dependent Fine-Grained Entity Type Tagging. ArXiv, abs/1412.1820.

Code

Our code is based on the following codebases:

GraphSage

We obtained the code from https://github.com/williamleif/graphsage-simple/.

Dense graph propagation

We obtained the code from https://github.com/yinboc/DGP