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Register a new userA Deep Learning System for Automatic Extraction of Typological Linguistic Information from Descriptive Grammars
نظام تعليمي عميق لاستخراج المعلومات اللغوية التلقائية من قواعد النمسات الوصفية
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النمطية اللغوية هي مجال من اللغويات المعنية بتحليل ومقارنة بين اللغات الطبيعية للعالم بناء على ميزاتها اللغوية المعينة. لهذا الغرض، تاريخيا، اعتمدت المنطقة على استخراج يدوية لقيم الميزات اللغوية من الأوصاف النصية للغات. هذا يجعلها مهمة شاقة وطولها باهظة الثمن وهي ملزمة أيضا بسعة الدماغ البشرية. في هذه الدراسة، نقدم نظام تعليمي عميق لمهمة الاستخراج التلقائي للميزات اللغوية من الأوصاف النصية للغات الطبيعية. أولا، يتم تسجيل أوصاف نصية يدويا مع هياكل خاصة تسمى الإطارات الدلالية. يتم تعلم تلك التعليقات التوضيحية من خلال شبكة عصبية متكررة، والتي تستخدم بعد ذلك لتعليق النص غير المشروح. أخيرا، يتم تحويل التعليقات التوضيحية إلى قيم ميزة اللغوية باستخدام وحدة نمطية قائمة على القاعدة. يتم استخدام Tembeddings Word، المستفادة من نص الأغراض العامة، كمصدر رئيسي للمعرفة من قبل الشبكة العصبية المتكررة. قارنا نظام التعلم العميق المقترح للنظام القائم على نظام التعلم الذي تم الإبلاغ عنه مسبقا لنفس المهمة، ويفوز نظام التعلم العميق من حيث درجات F1 بهامش عادل. من المتوقع أن يكون هذا النظام مساهمة مفيدة للجنة التلقائية لقواعد البيانات النموذجية، والتي يتم تطويرها يدويا.
Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the area has relied on manual extraction of linguistic feature values from textural descriptions of languages. This makes it a laborious and time expensive task and is also bound by human brain capacity. In this study, we present a deep learning system for the task of automatic extraction of linguistic features from textual descriptions of natural languages. First, textual descriptions are manually annotated with special structures called semantic frames. Those annotations are learned by a recurrent neural network, which is then used to annotate un-annotated text. Finally, the annotations are converted to linguistic feature values using a separate rule based module. Word embeddings, learned from general purpose text, are used as a major source of knowledge by the recurrent neural network. We compare the proposed deep learning system to a previously reported machine learning based system for the same task, and the deep learning system wins in terms of F1 scores with a fair margin. Such a system is expected to be a useful contribution for the automatic curation of typological databases, which otherwise are manually developed.
References used
https://aclanthology.org/
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by linguistic analysis modules. Errors in these features lead to errors of
relation detection and classification. In this work, we depart from these
traditional approaches w
ith complicated feature engineering by introducing
a convolutional neural network for relation extraction that automatically
learns features from sentences and minimizes the dependence on external
toolkits and resources. Our model takes advantages of multiple window
sizes for filters and pre-trained word embeddings as an initializer on a nonstatic
architecture to improve the performance.
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