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Register a new userTowards a Better Understanding of Noise in Natural Language Processing
نحو فهم أفضل للضوضاء في معالجة اللغة الطبيعية
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في هذه الورقة، نقترح تعريف وتعريفي من أنواع مختلفة من المحتوى النصي غير القياسي - يشار إليها عموما باسم الضوضاء "- في معالجة اللغة الطبيعية (NLP). في حين أن معالجة البيانات المسبقة هي بلا شك مهم بلا شك في NLP، خاصة عند التعامل مع المحتوى الذي تم إنشاؤه من قبل المستخدم، فإن فهم أوسع لمصادر الضوضاء المختلفة وكيفية التعامل معها هو جانب تم إهماله إلى حد كبير. نحن نقدم قائمة شاملة للمصادر المحتملة للضوضاء، وتصنيفها ووصفها، وتظهر تأثير مجموعة فرعية من استراتيجيات المعالجة القياسية المعدلة على مهام مختلفة. هدفنا الرئيسي هو زيادة الوعي بالمحتوى غير المعتاد - والذي لا ينبغي اعتباره دائما ضوضاء "- والحاجة إلى المعالجة المسبقة التي تعتمد على المهام. هذا بديل إلى بطانية، مثل الحلول الشاملة التي تطبقها الباحثون بشكل عام من خلال "خطوط أنابيب معالجة مسبقا مسبقا". النية هي لهذا التصنيف بمثابة نقطة مرجعية لدعم الباحثين NLP في وضع الاستراتيجيات لتنظيف أو تطبيع أو احتضان المحتوى غير المعتاد.
In this paper, we propose a definition and taxonomy of various types of non-standard textual content -- generally referred to as noise'' -- in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially when dealing with user-generated content, a broader understanding of different sources of noise and how to deal with them is an aspect that has been largely neglected. We provide a comprehensive list of potential sources of noise, categorise and describe them, and show the impact of a subset of standard pre-processing strategies on different tasks. Our main goal is to raise awareness of non-standard content -- which should not always be considered as noise'' -- and of the need for careful, task-dependent pre-processing. This is an alternative to blanket, all-encompassing solutions generally applied by researchers through standard'' pre-processing pipelines. The intention is for this categorisation to serve as a point of reference to support NLP researchers in devising strategies to clean, normalise or embrace non-standard content.
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