Cross-lingual Text Classification (CLC) consists of automatically classifying, according to a common set C of classes, documents each written in one of a set of languages L, and doing so more accurately than when “naively” classifying each document via its corresponding language-specific classifier. To obtain an increase in the classification accuracy for a given language, the system thus needs to also leverage the training examples written in the other languages. We tackle “multilabel” CLC via funnelling, a new ensemble learning method that we propose here. Funnelling consists of generating a two-tier classification system where all documents, irrespective of language, are classified by the same (second-tier) classifier. For this classifier, all documents are represented in a common, language-independent feature space consisting of the posterior probabilities generated by first-tier, language-dependent classifiers. This allows the classification of all test documents, of any language, to benefit from the information present in all training documents, of any language. We present substantial experiments, run on publicly available multilingual text collections, in which funnelling is shown to significantly outperform a number of state-of-the-art baselines. All code and datasets (in vector form) are made publicly available.
However, a key difference between the two methods is that stacking (like other ensemble methods such as bagging @cite_10 and boosting @cite_35 ) deals with ( homogeneous'') scenarios in which all training documents can in principle be represented in the same feature space and can thus concur to training the same classifier; in turn, this classifier can be used for classifying all the unlabelled documents. In stacking, the base classifiers sometimes differ in terms of the learning algorithm used to train them @cite_26 @cite_41 , or in terms of the subsets of the training set which are used for training them @cite_14 . In other words, in these scenarios setting up an ensemble is a choice, and not a necessity. It is instead a necessity in the ( heterogeneous'') scenarios which funnelling deals with, where labelled documents of different types (in our case: languages) could otherwise concur in training the same classifier (since they lie in different feature spaces), and where unlabelled documents could not (for analogous reasons) be classified by the same classifier.
@cite_35 @cite_14 @cite_26 @cite_41 @cite_10
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Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization. Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. Cross-language text categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper, we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular, in the proposed approach, we assume that a predefined category set and a collection of labeled training data is available for a given language L sub 1 . A classifier for a different language L sub 2 is trained by translating the available labeled training set for L sub 1 to L sub 2 and by using an additional set of unlabeled documents from L sub 2 . This technique allows us to extract correct statistical properties of the language L sub 2 which are not completely available in automatically translated examples, because of the different characteristics of language L sub 1 and of the approximation of the translation process. Our experimental results show that the performance of the proposed method is very promising when applied on a test document set extracted from newsgroups in English and Italian. Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classification noise have shown that bagging is fairly robust but that boosting is quite sensitive. Decorate is a recently introduced ensemble method that constructs diverse committees using artificial data. It has been shown to generally outperform both boosting and bagging when training data is limited. This paper compares the sensitivity of bagging, boosting, and Decorate to three types of imperfect data: missing features, classification noise, and feature noise. For missing data, Decorate is the most robust. For classification noise, bagging and Decorate are both robust, with bagging being slightly better than Decorate, while boosting is quite sensitive. For feature noise, all of the ensemble methods increase the resilience of the base classifier.