![]() 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. Finally, weĬonduct quantitative and qualitative analyses to explore important factors andĭifficulties in the task.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. ![]() We also propose two simple andĮffective models, which exploit different information of synsets. Present a novel task of automatic sememe prediction for synsets, aiming toĮxpand the seed dataset into a usable KB. ![]() Sememes for over $15$ thousand synsets (the entries of BabelNet). Serving as the seed of the multilingual sememe KB. On BabelNet, a multilingual encyclopedic dictionary. The issue, we propose to build a unified sememe KB for multiple languages based On only a few languages, which hinders their widespread utilization. Knowledge bases (KBs), which contain words annotated with sememes, have been Download a PDF of the paper titled Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets, by Fanchao Qi and 4 other authors Download PDF Abstract: A sememe is defined as the minimum semantic unit of human languages. ![]()
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