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Register a new userInvestigating Dominant Word Order on Universal Dependencies with Graph Rewriting
التحقيق في ترتيب الكلمات المهيمنة على التبعيات العالمية مع إعادة كتابة الرسم البياني
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تجارب تفاصيل الورقة هذه التي أجريناها في التبعيات العالمية 2.7 كورسا من أجل التحقيق في أمر الكلمات المهيمنة في اللغات المتاحة.لهذا الغرض، استخدمنا أداة إعادة كتابة الرسم البياني، نمت، والتي سمحت لنا بتجاوز التعليقات التوضيحية السطح وتحديد الموضوعات الضمنية.قمنا أولا بقياس توزيع أوامر الكلمة الستة المختلفة (SVO، SOV، VSO، VOS، OVS، OSV) في كورسا والتحقيق فيها عندما كان هناك فرق كبير في Corga بلغة معينة.بعد ذلك، قارننا النتائج التي تم الحصول عليها مع المعلومات المقدمة في قاعدة بيانات Wals (مجفف ومشبيلماث، 2013) وفي (̈Ostling، 2015).أخيرا، درسنا تأثير استخدام أداة إعادة كتابة الرسم البياني لهذه المهمة.تتوفر الأدوات والموارد المستخدمة لهذا البحث بحرية.
This paper details experiments we performed on the Universal Dependencies 2.7 corpora in order to investigate the dominant word order in the available languages. For this purpose, we used a graph rewriting tool, GREW, which allowed us to go beyond the surface annotations and identify the implicit subjects. We first measured the distribution of the six different word orders (SVO, SOV, VSO, VOS, OVS, OSV) in the corpora and investigated when there was a significant difference in the corpora within a given language. Then, we compared the obtained results with information provided in the WALS database (Dryer and Haspelmath, 2013) and in ( ̈Ostling, 2015). Finally, we examined the impact of using a graph rewriting tool for this task. The tools and resources used for this research are all freely available.
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This paper describes a system proposed for the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (EUD). We propose a Graph Rewriting based system for computing Enhanced Universal Dependencies, given the Basic Universal Dependencies (UD).
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 wit
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