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Becoming a proficient reader is a critical skill that supports future learning. Toward the end of the primary grades, reading becomes increasingly automatized, and children begin to transition from learning-to-read to reading-to-learn. Yet, the design of beginning reader books may be suboptimal for novice readers. Colorful illustrations that contain irrelevant information (i.e., seductive details) presented in close proximity to the text may increase attentional competition between these sources of information; thus, hampering decoding and reading comprehension. Study 1 examines this hypothesis by experimentally manipulating components of the book design (e.g., presence/absence of seductive details) and investigating its effect on attention and reading performance in first grade students. In Study 2, we conduct an analysis in which we identify common design features in books for beginning readers and examine the prevalence of design features that were fmmd to tax attention in Study 1 and in prior research. Collectively this work identifies an important opportunity in which instructional materials can be optimized to better support children as they learn-to-read.
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Deep Learning for Natural Language Processing: A Gentle Introduction
Upon encountering this publication, one might ask the obvious question, "Why do we need another deep learning and natural language processing book?" Several excellent ones have been published, covering both theoretical and practical aspects of deep learning and its application to language processing. However, from our experience teaching courses on natural language processing, we argue that, despite their excellent quality, most of these books do not target their most likely readers. The intended reader of this book is one who is skilled in a domain other than machine learning and natural language processing and whose work relies, at least partially, on the automated analysis of large amounts of data, especially textual data. Such experts may include social scientists, political scientists, biomedical scientists, and even computer scientists and computational linguists with limited exposure to machine learning. Existing deep learning and natural language processing books generally fall into two camps. The first camp focuses on the theoretical foundations of deep learning. This is certainly useful to the aforementioned readers, as one should understand the theoretical aspects of a tool before using it. However, these books tend to assume the typical background of a machine learning researcher and, as a consequence, I have often seen students who do not have this background rapidly get lost in such material. To mitigate this issue, the second type of book that exists today focuses on the machine learning practitioner; that is, on how to use deep learning software, with minimal attention paid to the theoretical aspects. We argue that focusing on practical aspects is similarly necessary but not sufficient. Considering that deep learning frameworks and libraries have gotten fairly complex, the chance of misusing them due to theoretical misunderstandings is high. We have commonly seen this problem in our courses, too. This book, therefore, aims to bridge the theoretical and practical aspects of deep learning for natural language processing. We cover the necessary theoretical background and assume minimal machine learning background from the reader. Our aim is that anyone who took introductory linear algebra and calculus courses will be able to follow the theoretical material. To address practical aspects, this book includes pseudo code for the simpler algorithms discussed and actual Python code for the more complicated architectures. The code should be understandable by anyone who has taken a Python programming course. After reading this book, we expect that the reader will have the necessary foundation to immediately begin building real-world, practical natural language processing systems, and to expand their knowledge by reading research publications on these topics. https://doi.org/10.1017/9781009026222
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- Award ID(s):
- 2006583
- PAR ID:
- 10550333
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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