Global text project : new horizons in textbook marketing

Purpose: The purpose of this paper is to introduce the global text project (GTP) case. The unique developments of the case provide insight of the many challenges and opportunities created within the open source movement.

Design/methodology/approach: A case study was used to illustrate some of the most pertinent and interesting developments in the field of marketing, alluding to the open source environment. A Wikibook was created in collaboration with all the participants of a graduate course and the development of this offering initiated a project called the GTP.

Findings: The open source movement has created new ways of thinking and acting. The contributions, modifications and improvements by all users to the original product provide a platform of continuous improvement and development.

Originality/value: The value of the paper lies in the lessons and challenges learnt from the case especially by those managing the GTP.

Reducing performance bias by intrinsically insensitive learning for unbalanced text mining

This thesis proposes three effective strategies to solve the significant performance-bias problem in imbalance text mining: (1) creation of a novel inexact field learning algorithm to overcome the dual-imbalance problem; (2) introduction of the one-class classification-framework to optimize classifier-parameters, and (3) proposal of a maximal-frequent-item-set discovery approach to achieve higher accuracy and efficiency.

Image to text translation by multi-label classification

This paper presents an image to text translation platform consisting of image segmentation, region features extraction, region blobs clustering, and translation components. A multi-label learning method is suggested for realizing the translation component. Empirical studies show that the predictive performance of the translation component is better than its counterparts when employed a dual-random ensemble multi-label classification algorithm.

Image to text translation by multi-label classification

This thesis includes the development of an architectural framework for the proposed image to text translation system containing four components. Selection of appropriate algorithms for the first three components developed three effective multi-label classification algorithms for the fourth component, i.e. the translation component, for different problem settings.

Web-text : perceptions of digital reading skills in the ESL classroom

New strategies required in Web reading and digital writing cause previous notions of literacy to be reshaped and compel teachers to rethink classroom reading practice. The aim of this paper is to compare student perceptions of reading skills needed in the traditional print- text mode with the skills needed to read and gather information on the Web. Do students perceive reading as different on the Web? Are there implications for reading classroom teachers? This research was conducted in a medium-sized suburban government primary school of 580 students from 72 different countries. The participants were 48 students in two grade-six classes, with a focus on 12 English as second language (ESL) students' responses. These students came from Taiwan, China, India, Malaysia, Poland and Bhutan. The study was replicated in an adult ELICOS language centre environment with the authors own class of 18 students from China, Indonesia, Korea, Taiwan, Thailand and Japan. Different student expectations of Web-text compared to paper-text were evident. This research adds to our constantly evolving notions of literacy embracing technology and can be applied to primary, secondary and tertiary levels of ESL teaching practice.

From site to text : Australian Aborigines and the origin of the family

Missions were not simply sites of modernity, they were also the source of key data for the modernist theories of human progress. The idea that so called “primitive peoples” provided a window to the origins of human institutions seemed axiomatic to nineteenth-century theorists of human society who sought evidence for these ideas from settlers, administrators and particularly missionaries. The 1870s and 1880s were the high point of missionary engagement with study-bound anthropologists, as questionnaires and letters were sent from the centres to the edges of empires. Missionary responses, augmented with settler and explorer observations, became the footnotes in early anthropological texts on “primitive” societies. These analyses were then mined for the foundation texts of the other social sciences in the late nineteenth century. Along with many other scholars, Karl Marx and Friedrich Engels read the anthropology of the period and slotted the findings into their analyses of human society.

Text clustering with important words using normalization

Important words, which usually exist in part of Title, Subject and Keywords, can briefly reflect the main topic of a document. In recent years, it is a common practice to exploit the semantic topic of documents and utilize important words to achieve document clustering, especially for short texts such as news articles. This paper proposes a novel method to extract important words from Subject and Keywords of articles, and then partition documents only with those important words. Considering the fact that frequencies of important words are usually low and the scale matrix dataset for important words is small, a normalization method is then proposed to normalize the scale dataset so that more accurate results can be achieved by sufficiently exploiting the limited information. The experiments validate the effectiveness of our method.

Text document clustering with metric learning

One reason for semi-supervised clustering fail to deliver satisfactory performance in document clustering is that the transformed optimization problem could have many candidate solutions, but existing methods provide no mechanism to select a suitable one from all those candidates. This paper alleviates this problem by posing the same task as a soft-constrained optimization problem, and introduces the salient degree measure as an information guide to control the searching of an optimal solution. Experimental results show the effectiveness of the proposed method in the improvement of the performance, especially when the amount of priori domain knowledge is limited.

Automating the creation of 3D animation from annotated fiction text

This paper describes a strategy for automatically converting fiction text into 3D animations. It assumes the existence of fiction text annotated with avatar, object, setting, transition and relation annotations, and presents a transformation process that converts annotated text into quantified constraint systems, the solutions to which are used in the population of 3D environments. Constraint solutions are valid over temporal intervals, ensuring that consistent dynamic behaviour is produced. A substantial level of automation is achieved, while providing opportunities for creative manual intervention in animation process. The process is demonstrated using annotated examples drawn from popular fiction text that are converted into animation sequences, confirming that the desired results can be achieved with only high-level human direction.

Constraint-based conversion of fiction text to a time-based graphical representation

This paper presents a method for converting unrestricted fiction text into a time-based graphical form. Key concepts extracted from the text are used to formulate constraints describing the interaction of entities in a scene. The solution of these constraints over their respective time intervals provides the trajectories for these entities in a graphical representation.

Three types of entity are extracted from fiction books to describe the scene, namely Avatars, Areas and Objects. We present a novel method for modeling the temporal aspect of a fiction story using multiple time-line representations after which the information extracted regarding entities and time-lines is used to formulate constraints. A constraint solving technique based on interval arithmetic is used to ensure that the behaviour of the entities satisfies the constraints over multiple universally quantified time intervals. This approach is demonstrated by finding solutions to multiple time-based constraints, and represents a new contribution to the field of Text-to-Scene conversion. An example of the automatically produced graphical output is provided in support of our constraint-based conversion scheme.

Evaluating parts-of-speech taggers for use in a text-to-scene conversion system

This paper presents parts-of-speech tagging as a first step towards an autonomous text-to-scene conversion system. It categorizes some freely available taggers, according to the techniques used by each in order to automatically identify word-classes. In addition, the performance of each identified tagger is verified experimentally. The SUSANNE corpus is used for testing and reveals the complexity of working with different tagsets, resulting in substantially lower accuracies in our tests than in those reported by the developers of each tagger. The taggers are then grouped to form a voting system to attempt to raise accuracies, but in no cases do the combined results improve upon the individual accuracies. Additionally a new metric, agreement, is tentatively proposed as an indication of confidence in the output of a group of taggers where such output cannot be validated.