Yeh, E., Ramage, D., Manning, C.D., Agirre, E., Soroa, A.: Wikiwalk: Random walks on wikipedia for semantic relatedness. Vossen, P.: EuroWordNet: A multilingual database of autonomous and language-specific WordNets connected via an inter-lingual index. Steinberger, R., Pouliquen, B., Hagman, J.: Cross-lingual document similarity calculation using the multilingual thesaurus EUROVOC. Romeo, S., Tagarelli, A., Ienco, D.: Semantic-Based Multilingual Document Clustering via Tensor Modeling. Ni, X., Sun, J., Hu, J., Chen, Z.: Cross lingual text classification by mining multilingual topics from wikipedia. Navigli, R., Ponzetto, S.P.: Multilingual WSD with just a few lines of code: the babelnet API. Navigli, R., Ponzetto, S.P.: Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Navigli, R., Lapata, M.: An experimental study of graph connectivity for unsupervised word sense disambiguation. Mihalcea, R., Tarau, P., Figa, E.: PageRank on semantic networks, with application to word sense disambiguation. Cambridge University Press, New York (2008) Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Liu, W., Chang, S.: Robust multi-class transductive learning with graphs. Klementiev, A., Titov, I., Bhattarai, B.: Inducing Crosslingual Distributed Representations of Words. Joachims, T.: Transductive Learning via Spectral Graph Partitioning. Joachims, T.: Transductive inference for text classification using support vector machines. Guo, Y., Xiao, M.: Transductive representation learning for cross-lingual text classification. Journal of the American Society for Information Science 41(6), 391–407 (1990)įranco-Salvador, M., Rosso, P., Navigli, R.: A knowledge-based representation for cross-language document retrieval and categorization. Springer, Heidelberg (2013)ĭeerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. Springer, Heidelberg (2014)ĭe Sousa, C.A.R., Rezende, S.O., Batista, G.E.A.P.A.: Influence of graph construction on semi-supervised learning. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C., de Jong, F., Radinsky, K., Hofmann, K. Knowl.-Based Syst. 50, 211–217 (2013)īarrón-Cedeño, A., Paramita, M.L., Clough, P., Rosso, P.: A comparison of approaches for measuring cross-lingual similarity of wikipedia articles. This process is experimental and the keywords may be updated as the learning algorithm improves.īarrón-Cedeño, A., Gupta, P., Rosso, P.: Methods for cross-language plagiarism detection. These keywords were added by machine and not by the authors. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. We resort to a state-of-the-art transductive learner to produce the document classification. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. Multilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities.
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