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.
In the absence of external parallel data, one polylingual DSM which has recently proved worthy (and that we use as a baseline in our experiments) is (LRI -- @cite_2 ), the polylingual extension of the (RI) method @cite_22 . RI is a context-counting model belonging to the family of random projection methods, and is considered a cheaper approximation of LSA @cite_47 . LRI is designed so that the orthogonality of the projection base is maximized, which allows to preserve sparsity and maximize the contribution of the information conveyed by the features shared across languages.
@cite_47 @cite_22 @cite_2
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Word space models enjoy considerable attention in current research on semantic indexing. Most notably, Latent Semantic Analysis Indexing (LSA LSI; , 1990, Landauer & Dumais, 1997) has become a household name in information access research, and deservedly so; LSA has proven its mettle in numerous applications, and has more or less spawned an entire research field since its introduction around 1990. Today, there is a rich flora of word space models available, and there are numerous publications that report exceptional results in many different applications, including information retrieval (, 1988), word sense disambiguation (Schutze, 1993), various semantic knowledge tests (, 1995, Karlgren & Sahlgren, 2001), and text categorization (Sahlgren & Karlgren, 2004). This paper introduces the Random Indexing word space approach, which presents an efficient, scalable and incremental alternative to standard word space methods. The paper is organized as follows: in the next section, we review the basic word space methodology. We then look at some of the problems that are inherent in the basic methodology, and also review some of the solutions that have been proposed in the literature. In the final section, we introduce the Random Indexing word space approach, and briefly review some of the experimental results that have been achieved with Random Indexing. This paper investigates the use of concept-based representations for text categorization. We introduce a new approach to create concept-based text representations, and apply it to a standard text categorization collection. The representations are used as input to a Support Vector Machine classifier, and the results show that there are certain categories for which concept-based representations constitute a viable supplement to word-based ones. We also demonstrate how the performance of the Support Vector Machine can be improved by combining representations.