Cross language text classification by model translation and semi-supervised learning (original) (raw)

Using Information from the Target Language to Improve Crosslingual Text Classification

Lecture Notes in Computer Science, 2010

Crosslingual text classification consists of exploiting labeled documents in a source language to classify documents in a different target language. In addition to the evident translation problem, this task also faces some difficulties caused by the cultural discrepancies manifested in both languages by means of different topic distributions. Such discrepancies make the classifier unreliable for the categorization task. In order to tackle this problem we propose to improve the classification performance by using information embedded in the own target dataset. The central idea of the proposed approach is that similar documents must belong to the same category. Therefore, it classifies the documents by considering not only their own content but also information about the assigned category to other similar documents from the same target dataset. Experimental results using three different languages evidence the appropriateness of the proposed approach.

Cross language text categorization by acquiring multilingual domain models from comparable corpora

Proceedings of the ACL Workshop on Building and Using Parallel Texts - ParaText '05, 2005

In a multilingual scenario, the classical monolingual text categorization problem can be reformulated as a cross language TC task, in which we have to cope with two or more languages (e.g. English and Italian). In this setting, the system is trained using labeled examples in a source language (e.g. English), and it classifies documents in a different target language (e.g. Italian).

Cross-effective cross-lingual document classification

This article addresses the question of how to deal with text categorization when the set of documents to be classified belong to different languages. The figures we provide demonstrate that cross-lingual classification where a classifier is trained using one language and tested against another is possible and feasible provided we translate a small number of words: the most relevant terms for class profiling. The experiments we report, demonstrate that the translation of these most relevant words proves to be a cost-effective approach to cross-lingual classification.

Using Machine Translators in Textual Data Classification

International Journal of Computer and Communication Engineering, 2012

In this paper, the effect of machine translators in the textual data classification is examined by using supervised classification methods. The developed system first analyzes and classifies an input text in one language, and then analyzes and classifies the same text in another language generated by machine translators from the input text. The obtained results are compared to measure the effect of the translators in textual data classification. The performances of the classification method used in this study are also measured and compared. The classification process can be described as training data preparation, feature selection, and classification of the input texts with/without translation. The obtained results show that Multinomial Naïve Bayes method is the most successful method, and that the translation has quite a small effect on the attained classification accuracy.

An EM Based Training Algorithm for Cross-Language Text Categorization

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005

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 1 . A classifier for a different language L 2 is trained by translating the available labeled training set for L 1 to L 2 and by using an additional set of unlabeled documents from L 2 . This technique allows us to extract correct statistical properties of the language L 2 which are not completely available in automatically translated examples, because of the different characteristics of language L 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.

Multilingual Text Classification through Combination of Monolingual Classifiers

Abstract. With the globalization trend there is a big amount of documents written in different languages. If these polylingual documents are already organized into existing categories one can deliver a learning model to classify newly arrived polylingual documents. Despite being able to adopt a simple approach by considering the problem as multiple independent monolingual text classification problems, this approach fails to use the opportunity offered by polylingual training documents to improve the effectiveness of the classifier.

Automatic Generation of Language-Independent Features for Cross-Lingual Classification

arXiv (Cornell University), 2018

Many applications require categorization of text documents using predefined categories. The main approach to performing text categorization is learning from labeled examples. For many tasks, it may be difficult to find examples in one language but easy in others. The problem of learning from examples in one or more languages and classifying (categorizing) in another is called cross-lingual learning. In this work, we present a novel approach that solves the general crosslingual text categorization problem. Our method generates, for each training document, a set of language-independent features. Using these features for training yields a languageindependent classifier. At the classification stage, we generate language-independent features for the unlabeled document, and apply the classifier on the new representation. To build the feature generator, we utilize a hierarchical language-independent ontology, where each concept has a set of support documents for each language involved. In the preprocessing stage, we use the support documents to build a set of language-independent feature generators, one for each language. The collection of these generators is used to map any document into the language-independent feature space. Our methodology works on the most general cross-lingual text categorization problems, being able to learn from any mix of languages and classify documents in any other language. We also present a method for exploiting the hierarchical structure of the ontology to create virtual supporting documents for languages that do not have them. We tested our method, using Wikipedia as our ontology, on the most commonly used test collections in cross-lingual text categorization, and found that it outperforms existing methods.

Cost-effective Cross-lingual Document Classification

2004

Cross-lingual Data Transformation and Combination for Text Classification

ArXiv, 2019

Text classification is a fundamental task for text data mining. In order to train a generalizable model, a large volume of text must be collected. To address data insufficiency, cross-lingual data may occasionally be necessary. Cross-lingual data sources may however suffer from data incompatibility, as text written in different languages can hold distinct word sequences and semantic patterns. Machine translation and word embedding alignment provide an effective way to transform and combine data for cross-lingual data training. To the best of our knowledge, there has been little work done on evaluating how the methodology used to conduct semantic space transformation and data combination affects the performance of classification models trained from cross-lingual resources. In this paper, we systematically evaluated the performance of two commonly used CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) text classifiers with differing data transformation and combinat...

Cross language text classification by model translation and semi-supervised learning (original) (raw)

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