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Register a new userSupertagging-based Parsing with Linear Context-free Rewriting Systems
التحليل القائم على السوبر مع أنظمة إعادة كتابة سياق خالية من السياق
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نقدم أول محلل محلل دائري على أساس أنظمة إعادة الكتابة الخالية من السياق (LCFRS).وهو يستخدم المصنفات العصبية والتفوق على المحللين السابقين في LCFRS في كل من الدقة وسرعة التحليل من هامش واسع.نتائجنا مواكبة أفضل المحللين المتساقين (العام)، وخاصة درجات الناخبين المتساقين إنشاء حالة جديدة من الفن.إن قلب نهجنا هو إجراء فعال لليككاليزي يدفع LCFRS معجمية من أي شجرة Treebank غير المتساقين.وصفنا بتعديل تحليل LCFRS المعتاد على الرسم البياني الذي يمثل التضخم بسرعة وإدخال إجراء يحول مشتقات LCFRS المعجمية إلى أشجار تحليل مكافئة من TreeBank الأصلي.يتم تقييم نهجنا على الانجليزية المتساقين بين بنك بنك بنسل وشركة النيجرية الألمانية والنمر.
We present the first supertagging-based parser for linear context-free rewriting systems (LCFRS). It utilizes neural classifiers and outperforms previous LCFRS-based parsers in both accuracy and parsing speed by a wide margin. Our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constituents establish a new state of the art. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. We describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure that transforms lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is evaluated on the English Discontinuous Penn Treebank and the German treebanks Negra and Tiger.
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