@inproceedings{riedl-biemann-2013-scaling,
title = "Scaling to Large{\mbox{$^3$}} Data: An Efficient and Effective Method to Compute Distributional Thesauri",
author = "Riedl, Martin and
Biemann, Chris",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1089",
pages = "884--890",
}
1. What is it?
They evaluated the similarity measures in Distributional Thesauri models.
2. What is amazing compared to previous works?
3. Where is the key to technologies and techniques?
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commented
4 years ago
0. Paper
@inproceedings{riedl-biemann-2013-scaling, title = "Scaling to Large{\mbox{$^3$}} Data: An Efficient and Effective Method to Compute Distributional Thesauri", author = "Riedl, Martin and Biemann, Chris", booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", month = oct, year = "2013", address = "Seattle, Washington, USA", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D13-1089", pages = "884--890", }
1. What is it?
They evaluated the similarity measures in Distributional Thesauri models.
2. What is amazing compared to previous works?
3. Where is the key to technologies and techniques?
They adopt LMI for calculating similarities.
4. How did evaluate it?
LMI is good for Distributional Thesauri model.
LMI is also good for large corpora.
5. Is there a discussion?
6. Which paper should read next?