--- title: "A FrameNet-Based Semantic Role Labeler for — Johansson 2006" description: "The paper tackles FrameNet-style semantic role labeling (SRL) for Swedish, a language without a sizeable FrameNet-annotated corpus." authors: ["Richard Johansson", "Pierre Nugues"] year: 2006 venue: "Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions" source: "https://aclanthology.org/P06-2057/" 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/johansson-2006-framenet-based-semantic-role-labeler/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/johansson-2006-framenet-based-semantic-role-labeler/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2006](https://md.research.elamur.ai/free/en/papers/2006/) / A FrameNet-Based Semantic Role Labeler for Swedish # A FrameNet-Based Semantic Role Labeler for Swedish **Authors:** [Richard Johansson](https://md.research.elamur.ai/free/en/authors/richard-johansson/), [Pierre Nugues](https://md.research.elamur.ai/free/en/authors/pierre-nugues/) · **Year:** 2006 · **Venue:** Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions · **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/P06-2057/) · [PDF](https://aclanthology.org/P06-2057.pdf) · [OpenAlex](https://openalex.org/W2139049564) **Citations:** 39 citations (openalex-snapshot · 2026-10-14) · [18](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary The paper tackles FrameNet-style semantic role labeling (SRL) for Swedish, a language without a sizeable FrameNet-annotated corpus. The authors avoid manual annotation by building the training data automatically. They first trained an English FrameNet labeler and ran it over the English half of Europarl. They then used Giza++ word alignments to map target words and frame element (FE) spans onto the Swedish half, cleaning the projected brackets with a few Swedish-specific heuristics about prepositions, conjunctions and chunk boundaries. No constituent parser existed for Swedish, and dependency subtrees often did not match FrameNet spans. They therefore designed two FE bracketing algorithms that classify chunks as opening or closing an FE while still drawing on dependency-parse features. For evaluation, they manually translated a sample of the FrameNet example corpus into Swedish. The system clearly beats baselines, though it still trails English systems. Its scores roughly equal the English labeler's quality multiplied by transfer accuracy, so the noise in the training data is the main bottleneck. It is presented as the first Swedish SRL system, and the approach should carry over to any language with an English parallel corpus. ## Key points - The English labeler was trained on 100,000 FrameNet example sentences covering the 150 most frequent frames, with a further 8,000 used for development. It used the Collins parser and SVM classifiers built with LIBSVM (Gaussian kernel), and scored precision 0.71 and recall 0.65 under strict scoring. - Projection to Swedish used Europarl, Giza++ alignments and the maximal span method. Sentences were kept only when the target word aligned to a Swedish noun, verb or adjective. - On the Swedish test set, classification accuracy on pre-segmented FEs was 0.75. The full task reached precision 0.67, recall 0.47 and F-measure about 0.55. - An expected-quality estimate multiplied the English scores by earlier transfer figures (P 0.84, R 0.81). It gives P 0.60, R 0.53 and F 0.56. The measured precision is notably higher than this estimate, suggesting the SVMs cope well with noisy training data. - Remaining weaknesses include low bracketing recall and weaker Swedish parsers. Europarl's frame distribution is also skewed, for example toward STATEMENT and DISCUSSION. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) ## Cited by Cited by 18 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 15 not yet. - [FrameNet on the Way to Babel: Creating a Bilingual FrameNet Using Wiktionary as Interlingual Connection](https://md.research.elamur.ai/free/en/papers/hartmann-2013-framenet-way-babel-creating-bilingual/) - [Semantic Role Labeling with the Swedish FrameNet](https://md.research.elamur.ai/free/en/papers/johansson-2012-semantic-role-labeling-swedish-framenet/) - [Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system](https://md.research.elamur.ai/free/en/papers/li-2014-cross-lingual-discourse-relation-analysis/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{johansson2006framenet, title = {{A FrameNet-Based Semantic Role Labeler for Swedish}}, author = {Johansson, Richard and Nugues, Pierre}, booktitle = {Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions}, year = {2006}, doi = {10.3115/1273073.1273130}, url = {https://doi.org/10.3115/1273073.1273130} } ``` - [← previous A Factored Functional Dependency Transformation of the English Penn Treebank for Probabilistic Surface Generation](https://md.research.elamur.ai/free/en/papers/geary-2006-factored-functional-dependency-transformation-english/) - [next → A Hebrew Tree Bank Based on Cantillation Marks](https://md.research.elamur.ai/free/en/papers/lingbulk-2006-wu-hebrew-tree-bank-based-cantillation/) - [↑ back to papers of 2006](https://md.research.elamur.ai/free/en/papers/2006/) ## References 16 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Collin F. Baker, Charles J. Fillmore, and John B. Lowe. 1998. The Berkeley FrameNet Project. In Proceedings of COLING-ACL’98, pages 86–90, Montréal, Canada. [find](https://scholar.google.com/scholar?q=Collin+F%2E+Baker%2C+Charles+J%2E+Fillmore%2C+and+John+B%2E+Lowe%2E+1998%2E+The+Berkeley+FrameNet+Project%2E+In+Proceedings+of+COLING%2DACL%E2%80%9998%2C+pages+86%E2%80%9390%2C+Montr%C3%A9al%2C+Canada%2E) [our card](https://md.research.elamur.ai/free/en/papers/lex-1998-baker-berkeley-framenet/) 2. Chih-Chung Chang and Chih-Jen Lin, 2001. LIBSVM: a library for support vector machines. [find](https://scholar.google.com/scholar?q=Chih%2DChung+Chang+and+Chih%2DJen+Lin%2C+2001%2E+LIBSVM%3A+a+library+for+support+vector+machines%2E) 3. Michael J. Collins. 1999. Head-driven statistical models for natural language parsing. Ph.D. thesis, University of Pennsylvania, Philadelphia. 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[find](https://scholar.google.com/scholar?q=Ken+Litkowski%2E+2004%2E+Senseval%2D3+task%3A+Automatic+labeling+of+semantic+roles%2E+In+Senseval%2D3%3A+Third+International+Workshop+on+the+Evaluation+of+Systems+for+the+Semantic+Analysis+of+Text%2C+pages+9%E2%80%9312%2E) [our card](https://md.research.elamur.ai/free/en/papers/gildea-2002-automatic-labeling-semantic-roles/) 11. Joakim Nivre, Johan Hall, and Jens Nilsson. 2004. Memory-based dependency parsing. In Proceedings of CoNLL-2004, pages 49–56. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2C+Johan+Hall%2C+and+Jens+Nilsson%2E+2004%2E+Memory%2Dbased+dependency+parsing%2E+In+Proceedings+of+CoNLL%2D2004%2C+pages+49%E2%80%9356%2E) [our card](https://md.research.elamur.ai/free/en/papers/nivre-2004-memory-based-dependency-parsing/) 12. Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1):19–51. [↗](https://doi.org/10.1162/089120103321337421) 13. 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