---
title: "Cross-Language Text Classification Using Structural"
description: "The paper addresses cross-language text classification: training a classifier for a… 82% accuracy. 27 references, cited by 72 papers here."
authors: ["Peter Prettenhofer", "Benno Stein"]
year: 2010
venue: "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P10-1114/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/prettenhofer-2010-cross-language-text-classification-structural/"
---
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# Cross-Language Text Classification Using Structural Correspondence Learning

**Authors:** Peter Prettenhofer and [Benno Stein](https://md.research.elamur.ai/free/en/authors/benno-stein/) · **Year:** 2010 · **Venue:** [Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2010](https://md.research.elamur.ai/free/en/venues/acl/2010/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P10-1114/) · [PDF](https://aclanthology.org/P10-1114.pdf) · [OpenAlex](https://openalex.org/W2171068337)

**Citations:** 241 citations (OpenAlex, 2026-10-14) · [72](https://md.research.elamur.ai/free/en/papers/prettenhofer-2010-cross-language-text-classification-structural/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper addresses cross-language text classification: training a classifier for a target language that has no labeled data, using labeled documents from a source language. The authors adapt structural correspondence learning (SCL), originally a domain adaptation method, to this setting and call it CL-SCL. Each language is treated as its own domain. A small set of pivots, which are word pairs translated by a simple word translation oracle, links the two languages. For each pivot, a linear classifier is trained on unlabeled documents from both languages to predict whether the pivot occurs. The learned weights are then compressed with SVD into a shared low-dimensional representation. Experiments cover sentiment classification on corpora the authors compiled, transferring from English into three target languages: German, French, and Japanese. CL-SCL comes close to a machine-translation baseline (CL-MT) for the European languages and beats it for Japanese. This matters because the method needs only unlabeled text and a few hundred word translations, with no parallel corpus or MT system.

## Key points
- The pivots are 100–500 translated word pairs. The vocabularies on each side are on the order of 10^5 words.
- Default hyperparameters were m = 450 pivots, k = 100 dimensions, and minimum pivot support φ = 30.
- The monolingual upper bound averages about 82% accuracy. CL-MT falls 9.9% below it for Japanese but only 5.3% below it for German and French.
- **German and French:** CL-MT usually wins, but mostly by less than 1%. The gap reaches about 2% only for French book and music reviews.
- **Japanese:** CL-SCL beats CL-MT by roughly 3% on most tasks. It does even better with fewer pivots (150 \< m \< 250).
- **Unlabeled data:** more unlabeled data helps, but gains level off at a labeled-to-unlabeled ratio of about 10 across all nine tasks. A ratio of 1 corresponds to 2,000 unlabeled documents per language.
- **Dimensionality:** k between 75 and 150 works equally well regardless of task.

## Interesting ideas
- **Language transfer as domain adaptation.** Treating each language as a separate domain lets existing domain adaptation tools work across languages. This could help low-resource languages such as Ukrainian, where parallel data is scarce.
- **Pivot-occurrence prediction as auxiliary tasks.** Predicting whether a translated word pair appears in a document turns unlabeled text into many cheap supervised problems. The shared structure learned from these problems becomes a bridge between the languages, and the same trick could be reused for any feature set with a few known anchors.
- **Choosing pivots for the task.** Pivots are selected with the target task in mind, so the induced correspondences capture task-specific usage, not just dictionary meaning. For example, "beautiful" in book reviews correlates with illustrations and poetry. Pivots chosen this way can encode domain pragmatics that general bilingual resources miss.
- **Checking learned correspondences qualitatively.** Listing the words most correlated with each pivot, and separating meaning-based from usage-based correlations, is a simple way to see what a representation has learned.
- **Comparing against MT and a monolingual upper bound.** Reporting both shows where translation quality limits performance. In this paper, the MT gap was much larger for Japanese, and that is exactly where CL-SCL helped most. This kind of comparison is a good diagnostic for distant language pairs.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Bauhaus-Universität Weimar](https://md.research.elamur.ai/free/en/universities/bauhaus-universitat-weimar/) — University, Weimar, Germany · 9 works · 3,111 citations

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## Cite

Cite as: Peter Prettenhofer and Benno Stein. 2010. Cross-Language Text Classification Using Structural Correspondence Learning. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{prettenhofer2010cross,
  title = {{Cross-Language Text Classification Using Structural Correspondence Learning}},
  author = {Peter Prettenhofer and Benno Stein},
  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
  year = {2010},
  url = {https://aclanthology.org/P10-1114/}
}
```

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## References

27 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*