Text classification using string kernels

2006

Traditional bag-of-words model and recent wordsequence kernel are two well-known techniques in the field of text categorization. Bag-of-words representation neglects the word order, which could result in less computation accuracy for some types of documents. Word-sequence kernel takes into account word order, but does not include all information of the word frequency. A weighted kernel model that combines these two models was proposed by the authors [1]. This paper is focused on the optimization of the weighting parameters, which are functions of word frequency. Experiments have been conducted with Reuter's database and show that the new weighted kernel achieves better classification accuracy.

2003

We address the problem of categorising documents using kernel-based methods such as Support Vector Machines. Since the work of Joachims (1998), there is ample experimental evidence that SVM using the standard word frequencies as features yield state-of-the-art performance on a number of benchmark problems. Recently, Lodhi et al. (2002) proposed the use of string kernels, a novel way of computing document similarity based of matching non-consecutive subsequences of characters. In this article, we propose the use of this technique with sequences of words rather than characters. This approach has several advantages, in particular it is more efficient computationally and it ties in closely with standard linguistic pre-processing techniques. We present some extensions to sequence kernels dealing with symbol-dependent and match-dependent decay factors, and present empirical evaluations of these extensions on the Reuters-21578 datasets.

Proceedings of the 2009 International Joint Conference on Neural Networks, 2009

Text classification by support vector machines can benefit from semantic smoothing kernels that regard semantic relations among index terms while computing similarity. Adding expansion terms to the vector representation can also improve effectiveness. However, existing semantic smoothing kernels do not employ term expansion. This paper proposes a new nonlinear kernel for text classification to exploit semantic relations between terms to add weighted expansion terms.

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20newsgroups database. Support Vector Machines provide the best accuracy on test data.

Engineering Applications of Artificial Intelligence, 2015

Text categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their ability to handle high dimensional and sparse data which is the prohibitive characteristics of textual data representation. Recently, there is an increased interest in the exploitation of background knowledge such as ontologies and corpus-based statistical knowledge in text categorization. It has been shown that, by replacing the standard kernel functions such as linear kernel with customized kernel functions which take advantage of this background knowledge, it is possible to increase the performance of SVM in the text classification domain. Based on this, we propose a novel semantic smoothing kernel for SVM. The suggested approach is based on a meaning measure, which calculates the meaningfulness of the terms in the context of classes. The documents vectors are smoothed based on these meaning values of the terms in the context of classes. Since we efficiently make use of the class information in the smoothing process, it can be considered a supervised smoothing kernel. The meaning measure is based on the Helmholtz principle from Gestalt theory and has previously been applied to several text mining applications such as document summarization and feature extraction. However, to the best of our knowledge, ours is the first study to use meaning measure in a supervised setting to build a semantic kernel for SVM. We evaluated the proposed approach by conducting a large number of experiments on well-known textual datasets and present results with respect to different experimental conditions. We compare our results with traditional kernels used in SVM such as linear kernel as well as with several corpus-based semantic kernels. Our results show that classification performance of the proposed approach outperforms other kernels.

2012

This work concerns a comparison of SVM kernel methods in text categorization tasks. In particular I define a kernel function that estimates the similarity between two objects computing by their compressed lengths. In fact, compression algorithms can detect arbitrarily long dependencies within the text strings. Data text vectorization looses information in feature extractions and is highly sensitive by textual language. Furthermore, these methods are language independent and require no text preprocessing. Moreover, the accuracy computed on the datasets (Web-KB, 20ng and Reuters-21578), in some case, is greater than Gaussian, linear and polynomial kernels. The method limits are represented by computational time complexity of the Gram matrix and by very poor performance on non-textual datasets.

Proceedings of the Ninth Conference on Computational Natural Language Learning - CONLL '05, 2005

In this paper we propose and evaluate a technique to perform semi-supervised learning for Text Categorization.

Formal Pattern Analysis & Applications, 2004

The bag-of-words approach to text document representation typically results in vectors of the order of 5000 to 20000 components as the representation of documents. In order to make effective use of various statistical classifiers, it may be necessary to reduce the dimensionality of this representation. We point out deficiencies in class discrimination of two popular such methods, Latent Semantic Indexing (LSI), and sequential feature selection according to some relevant criterion.

Text categorization is the task of assigning predefined categories to natural language text. With the widely used "bag-ofword" representation, previous researches usually assign a word with values that express whether this word appears in the document concerned or how frequently this word appears. Although these values are useful for text categorization, they have not fully expressed the abundant information contained in the document. This paper explores the effect of other types of values, which express the distribution of a word in the document. These novel values assigned to a word are called distributional features, which include the compactness of the appearances of the word and the position of the first appearance of the word. The proposed distributional features are exploited by a tfidf style equation, and different features are combined using ensemble learning techniques. Experiments show that the distributional features are useful for text categorization. In contrast to using the traditional term frequency values solely, including the distributional features requires only a little additional cost, while the categorization performance can be significantly improved. Further analysis shows that the distributional features are especially useful when documents are long and the writing style is casual.

2008

Abstract Standard Machine Learning approaches to text classification use the bag-of-words representation of documents to deceive the classification target function. Typical linguistic structures such as morphology, syntax and semantic are completely ignored in the learning process. This paper examines the role of these structures on the classifier construction applying the study to the Portuguese language. Classifiers are built using the SVM algorithm on a newspaper's articles dataset.