[Enhancing Web Pages Readabilty For Non Native Readers]

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ACKNOWLEDGEMENT

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

Signed __________________ Date _________________

ABSTRACT

Reading is an integral part of educational development; however, it is frustrating for people who struggle to understand (are not motivated to read, respectively) text documents that are beyond (below, respectively) their readability levels. Finding appropriate reading materials, with or without first scanning through their contents, is a challenge, since there are tremendous amount of documents these days and a clear majority of them are not tagged with their readability levels. Even though existing readability assessment tools determine readability levels of text documents, they analyze solely the lexical, syntactic, and/or semantic properties of a document, which are neither fully-automated, generalized, nor well-defined and are mostly based on observations. To advance the current readability analysis technique, we propose a robust, fully-automated readability analyzer, denoted ReadAid, which employs support vector machines to combine features from the UK Curriculum and College Board, traditional readability measures, and the author(s) and subject area(s) of a text document d to assess the readability level of d. WebReader can be applied for (i) filtering documents (retrieved in response to a web query) of a particular readability level, (ii) determining the readability levels of digitalized text documents, such as book chapters, magazine articles, and news stories, or (iii) dynamically analyzing, in real time, the grade level of a text document being created. The novelty of WebReader lies on using authorship, subject areas, and academic concepts and grammatical constructions extracted from the UK Curriculum to determine the readability level of a text document. Experimental results show that WebReader is highly effective and outperforms existing state-of-the-art readability assessment tools.

Table of Contents

ACKNOWLEDGEMENTII

DECLARATIONIII

ABSTRACTIV

SECTION 12

Aims and Objectives of the Study3

SECTION 2: INITIAL DESIGN3

User profile Analysis4

Usability Requirements4

An Initial set of Task requirements6

SECTION 3: STATE-OF-THE ART REVIEW6

SECTION 4: DETAILED DESIGN8

Topical Density9

SECTION 5: CRITICAL EVALUATION10

Future Work10

REFERENCES11

Section 1

According to a report published by the UK media, in 2009, 1 in every 7 adults, i.e., 32 million, across the UK had low literacy skills. Another report prepared by the National Centre for Education claims that in 2008 30% of students in public schools across the UK were below basic achievement in reading. At present, schools in the UK (i) rely on expert educators to determine the readability level of a given text and (ii) offer students lists and directories of hand-picked digitalized (text/pdf/rtf/doc) documents and web (HTML/XML) pages appropriate for their grade levels (Ateniese, 2006, 86).

We will develop an automated readability assessment tool, denoted WebReader , which assists (i) teachers in developing educational materials that are suitable for their students at a particular readability ...