KeLP: a Kernel-based Learning Platform for Natural Language Processing

Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.

[1]  Thorsten Joachims,et al.  Making large scale SVM learning practical , 1998 .

[2]  Dan Roth,et al.  Learning Question Classifiers , 2002, COLING.

[3]  Alessandro Moschitti,et al.  Automatic Feature Engineering for Answer Selection and Extraction , 2013, EMNLP.

[4]  Claudio Gentile,et al.  Tracking the best hyperplane with a simple budget Perceptron , 2006, Machine Learning.

[5]  Alessandro Moschitti,et al.  Structural Representations for Learning Relations between Pairs of Texts , 2015, ACL.

[6]  Matthieu Cord,et al.  JKernelMachines: a simple framework for kernel machine , 2013, J. Mach. Learn. Res..

[7]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[8]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[9]  Yiming Yang,et al.  RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..

[10]  Alessandro Moschitti,et al.  Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees , 2006, ECML.

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Aravind K. Joshi,et al.  An SVM-based voting algorithm with application to parse reranking , 2003, CoNLL.

[13]  Michael Collins,et al.  Convolution Kernels for Natural Language , 2001, NIPS.

[14]  Razvan C. Bunescu,et al.  Subsequence Kernels for Relation Extraction , 2005, NIPS.

[15]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[16]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[19]  Preslav Nakov,et al.  SemEval-2013 Task 2: Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[20]  Yoram Singer,et al.  Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..

[21]  Roberto Basili,et al.  Structured Lexical Similarity via Convolution Kernels on Dependency Trees , 2011, EMNLP.

[22]  Xavier Carreras,et al.  Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling , 2005, CoNLL.

[23]  M. Pennacchiotti,et al.  A machine learning approach to textual entailment recognition , 2009, Natural Language Engineering.

[24]  Roberto Basili,et al.  Semantic Compositionality in Tree Kernels , 2014, CIKM.

[25]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.