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Register a new userPart-of-speech tagging of Swedish texts in the neural era
جزء من الكلام العلامات من النصوص السويدية في الحقبة العصبية
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نحن ندرب ونختبر خمسة علامات مفتوحة المصدر، والتي تستخدم أساليب مختلفة، على ثلاثة كوربورا السويدية، وهي ذات حجم مماثل ولكن استخدام أشكال مختلفة.يحقق Tagger KB-Bert Tagger أعلى دقة لعلامات جزء من الكلام والمورفولوجية، بينما تكون سريعة بما يكفي للاستخدام العملي.نحن نقارن أيضا الأداء عبر الأشرقة وعبر الأنواع المختلفة في إحدى الشركات.نقوم بإجراء تحليل الأخطاء اليدوي وأداء تحليل إحصائي للعوامل التي تؤثر على مدى صعوبة علامات محددة.أخيرا، نقوم باختبار أساليب الفرقة، والتي تبين أن التحسن الصغير (ولكن غير مهم) على أفضل أداء يمكن تحقيقه.
We train and test five open-source taggers, which use different methods, on three Swedish corpora, which are of comparable size but use different tagsets. The KB-Bert tagger achieves the highest accuracy for part-of-speech and morphological tagging, while being fast enough for practical use. We also compare the performance across tagsets and across different genres in one of the corpora. We perform manual error analysis and perform a statistical analysis of factors which affect how difficult specific tags are. Finally, we test ensemble methods, showing that a small (but not significant) improvement over the best-performing tagger can be achieved.
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Active learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristi
cs are generally designed on the principle of selecting uncertain yet representative training instances, where annotating these instances may reduce a large number of errors. However, in an empirical study across six typologically diverse languages (German, Swedish, Galician, North Sami, Persian, and Ukrainian), we found the surprising result that even in an oracle scenario where we know the true uncertainty of predictions, these current heuristics are far from optimal. Based on this analysis, we pose the problem of AL as selecting instances that maximally reduce the confusion between particular pairs of output tags. Extensive experimentation on the aforementioned languages shows that our proposed AL strategy outperforms other AL strategies by a significant margin. We also present auxiliary results demonstrating the importance of proper calibration of models, which we ensure through cross-view training, and analysis demonstrating how our proposed strategy selects examples that more closely follow the oracle data distribution. The code is publicly released here.1
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