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Register a new userText Preprocessing and its Implications in a Digital Humanities Project
إعادة النظر في النص وآثارها في مشروع العلوم الإنسانية الرقمية
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تركز هذه الورقة على تنظيف البيانات كجزء من إجراء مسبق مسبق تطبق على البيانات النصية المستردة من الويب. على الرغم من أن أهمية هذه المرحلة المبكرة في مشروع باستخدام أساليب NLP غالبا ما يسلط الضوء عليها من قبل الباحثون، فإن التفاصيل، والمبادئ والتقنيات العامة عادة ما يتم استبعادها بسبب النظر في الفضاء. في أحسن الأحوال، يتم رفضهم بتعليق تم تطبيق إجراءات تنظيف البيانات المعتادة ومعالجتها المعتميات ". عادة ما يتم إعطاء المزيد من التغطية الشرح النص التلقائي النصي مثل Lemmatisation ووضع العلامات والتحليلات الجزئية والتحليلات، والتي غالبا ما يتم تضمينها في Preprocessing. في الأدب، يتم استخدام مصطلح المعالجة المسبق "للإشارة إلى مجموعة واسعة من الإجراءات، من التصفية والتنظيف لتحويل البيانات مثل التمثيل الناتج والرقم، مما قد يخلق الارتباك. نقول أن إعادة معالجة النصوص قد تشوه توزيع البيانات الأصلية فيما يتعلق بالبيانات الوصفية، مثل أنواع المواقع والأوقات وأوقات البيانات المسجلة. في هذه الورقة، نصف نهجا منهجيا لتنظيف البيانات النصية الملغومة من قبل شركة لتوفير البيانات لبرنامج العلوم الإنسانية الرقمية (DH) التي تركز على التحليلات الثقافية. نحن نكشف عن أنواع وكمية الضوضاء في البيانات الواردة من مصادر الويب المختلفة وتقدير التغييرات في حجم البيانات المرتبطة بالموافقة المسبقة. نحن أيضا مقارنة نتائج تجربة تصنيف النص يعمل على البيانات الخام ومعالجتها. نأمل أن تساعد تجربتنا ونهجنا على مساعدة مجتمع DH لتشخيص جودة البيانات النصية التي تم جمعها من الويب وإعدادها لمزيد من معالجة اللغة الطبيعية.
This paper focuses on data cleaning as part of a preprocessing procedure applied to text data retrieved from the web. Although the importance of this early stage in a project using NLP methods is often highlighted by researchers, the details, general principles and techniques are usually left out due to consideration of space. At best, they are dismissed with a comment The usual data cleaning and preprocessing procedures were applied''. More coverage is usually given to automatic text annotation such as lemmatisation, part-of-speech tagging and parsing, which is often included in preprocessing. In the literature, the term preprocessing' is used to refer to a wide range of procedures, from filtering and cleaning to data transformation such as stemming and numeric representation, which might create confusion. We argue that text preprocessing might skew original data distribution with regard to the metadata, such as types, locations and times of registered datapoints. In this paper we describe a systematic approach to cleaning text data mined by a data-providing company for a Digital Humanities (DH) project focused on cultural analytics. We reveal the types and amount of noise in the data coming from various web sources and estimate the changes in the size of the data associated with preprocessing. We also compare the results of a text classification experiment run on the raw and preprocessed data. We hope that our experience and approaches will help the DH community to diagnose the quality of textual data collected from the web and prepare it for further natural language processing.
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gives justifications and arguments of the States project owners a Zionist-American intervention in the affairs of Arab countries, and the violation of their sovereignty and control over their own resources, whether oil or gas, or take advantage of its strategic location in ways to control global trade. ...
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