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Register a new userOn Writing a Textbook on Natural Language Processing
عند كتابة كتاب مدرسي في معالجة اللغة الطبيعية
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هناك الآلاف من الأوراق حول معالجة اللغة الطبيعية واللغويات الحاسوبية، ولكن عدد قليل جدا من الكتب المدرسية.أصف الدافع والعملية لكتابة كتاب مدرسي في كلية حول معالجة اللغة الطبيعية، وتقديم المشورة والتشجيع للقراء الذين قد يهتمون بكتابة كتاب مدرسي خاص بهم.
There are thousands of papers about natural language processing and computational linguistics, but very few textbooks. I describe the motivation and process for writing a college textbook on natural language processing, and offer advice and encouragement for readers who may be interested in writing a textbook of their own.
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The field of Natural Language Processing (NLP) changes rapidly, requiring course offerings to adjust with those changes, and NLP is not just for computer scientists; it's a field that should be accessible to anyone who has a sufficient background. In
this paper, I explain how students with Computer Science and Data Science backgrounds can be well-prepared for an upper-division NLP course at a large state university. The course covers probability and information theory, elementary linguistics, machine and deep learning, with an attempt to balance theoretical ideas and concepts with practical applications. I explain the course objectives, topics and assignments, reflect on adjustments to the course over the last four years, as well as feedback from students.
In this paper we present a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks, every week consists of lectures, practical sessions and quiz assigments. Three weeks out o
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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 wh
en 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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