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Register a new userLearning Syntax from Naturally-Occurring Bracketings
بناء جملة من الأقواس التي تحدث بشكل طبيعي
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يمكن أن تعكس القواط التي تحدث بشكل طبيعي، مثل الإجابة على شظايا لأسئلة اللغة الطبيعية والارتباطات التشعبية على صفحات الويب، الحد من الحدس النحامي البشري فيما يتعلق بحدود الجمل الفعلية.إن توفرهم والمراسلات التقريبية لبناء الجملة يجعلهم جذابا على أنها مصادر معلومات بعيدة لتدميرها في تحليل دائم غير مخالف.لكنهم صاخبة وغير كاملة.لمعالجة هذا التحدي، نقوم بتطوير فقدان منحدري من الأقواس الجزئي في التعلم.توضح التجارب أن نماذجنا الخاضعة للإشراف على نطاق واسع تدرب على البيانات الموجودة بشكل طبيعي قوسين أكثر دقة في حث الهياكل النحوية من النظم المنافسة غير المنفصلة.على English WSJ Corpus، تحقق نماذجنا درجة F1 غير قابلة للتحقيق من 68.9 لتحليل الدوائر الانتخابية.
Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsupervised constituency parsing. But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning. Experiments demonstrate that our distantly-supervised models trained on naturally-occurring bracketing data are more accurate in inducing syntactic structures than competing unsupervised systems. On the English WSJ corpus, our models achieve an unlabeled F1 score of 68.9 for constituency parsing.
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