Sprucing up the trees: error detection in treebanks
We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treeba...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Kapitel/Artikel Konferenzschrift |
| Sprache: | Englisch |
| Veröffentlicht: |
August 2018
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| In: |
The 27th International Conference on Computational Linguistics - proceedings of the conference
Year: 2018, Pages: 107-118 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://www.aclweb.org/anthology/C18-1010
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| Verfasserangaben: | Ines Rehbein, Josef Ruppenhofer |
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| 520 | |a We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data. | ||
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