Cross-Language Text Classification Using Structural Correspondence Learning

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1118–1127, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Cross-Language Text Classification using Structural Correspondence Learning Peter Prettenhofer and Benno Stein Bauhaus-Universit¨at Weimar D-99421 Weimar, Germany {peter.prettenhofer,benno.stein}@uni-weimar.de Abstract We present a new approach to cross- language text classification that builds on structural correspondence learning, a re- cently proposed theory for domain adap- tation. The approach uses unlabeled doc- uments, along with a simple word trans- lation oracle, in order to induce task- specific, cross-lingual word correspon- dences. We report on analyses that reveal quantitative insights about the use of un- labeled data and the complexity of inter- language correspondence modeling. We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as tar- get languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas. 1 Introduction This paper deals with cross-language text classifi- cation problems. The solution of such problems requires the transfer of classification knowledge between two languages. Stated precisely: We are given a text classification task γ in a target lan- guage T for which no labeled documents are avail- able. γ may be a spam filtering task, a topic cate- gorization task, or a sentiment classification task. In addition, we are given labeled documents for the identical task in a different source language S. Such type of cross-language text classification problems are addressed by constructing a clas- sifier fS with training documents written in S and by applying fS to unlabeled documents writ- ten in T . For the application of fS under lan- guage T different approaches are current practice: machine translation of unlabeled documents from T to S, dictionary-based translation of unlabeled documents from T to S, or language-independent concept modeling by means of comparable cor- pora. The mentioned approaches have their pros and cons, some of which are discussed below. Here we propose a different approach to cross- language text classification which adopts ideas from the field of multi-task learning (Ando and Zhang, 2005a). Our approach builds upon struc- tural correspondence learning, SCL, a recently proposed theory for domain adaptation in the field of natural language processing (Blitzer et al., 2006). Similar to SCL, our approach induces corre- spondences among the words from both languages by means of a small number of so-called pivots. In our context a pivot is a pair of words, {wS, wT }, from the source language S and the target lan- guage T , which possess a similar semantics. Test- ing the occurrence of wS or wT in a set of unla- beled documents from S and T yields two equiv- alence classes across these languages: one class contains the documents where either wS or wT oc- cur, the other class contains the documents where neither wS nor wT occur. Ideally, a pivot splits the set of unlabeled documents with respect to the semantics that is associated with …

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