splitSVM: Fast, Space-Efficient, non-Heuristic, Polynomial Kernel Computation for NLP Applications

By Yoav Goldberg, Michael Elhadad. 2008

94 citations recorded by the source corpus.

We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cho-Jui Hsieh
2. Chih-Jen Lin
3. Kai-Wei Chang
4. Peter Richtárik
5. Junsong Yuan
6. Tristan Naumann
7. Martin Jaggi
8. Mohit Bansal

In Information Systems

1. Cho-Jui Hsieh
2. Chih-Jen Lin
3. Yuan Luo
4. Kai-Wei Chang

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “In the reviewed papers, SVMs constantly outperform other learning algorithms for computational phenotyping [27, 89, 99, 118, 127].”
• Kervolutional Neural Networks (2019, background): “Polynomial Kervolution Although the existing literatures have shown that the polynomial kernel (10) works well for the problem of natural language processing (NLP) when d p = 2 using SVM [17], we find its performance is better when d p = 3 in KNN for the problem of image recognition. where d p ( d p ”
• Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (2010, background): “Some have explored the fast calculation of low-degree polynomial kernels to save the testing time (e.g., Isozaki and Kazawa, 2002; Kudo and Matsumoto, 2003; Goldberg and Elhadad, 2008).”