Identifying annotations for adventure game generation from fiction text

Recent advancements in Text-to-Scene research have lead to the development of systems which automatically extract key concepts from the text of a fiction book and generate computer animated movies depicting the story. Extracting such annotations from raw fiction text is a laborious process and so in this work we evaluate appropriate candidates to serve as the basis for the required annotations for generating interactive virtual worlds.

We validate our choice by generating adventure games: inter-active virtual worlds which create a stylized representation of the environment described in the text, populate it with characters related to the story and define game goals related to the plot of the fiction story. Our prototype produces a fully playable game, making use of an existing open-source game engine.

The process is evaluated using user tests in which participants are asked to measure the accuracy with which the game represents the events, characters and goals described in the story. The response indicates that the chosen annotation set is sufficient to define a game that is a plausibly acceptable representation of the text.

A method for automatically creating 3D animated scenes 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 highlevel human direction.

Improving reliability of unbalanced text mining by reducing performance bias

Class imbalance in textual data is one important factor that affects the reliability of text mining. For imbalanced textual data, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This chapter aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one class classifiers achieve more balanced performance than the standard approaches.

Prediction of age, sentiment, and connectivity from social media text

Social media corpora, including the textual output of blogs, forums, and messaging applications, provide fertile ground for linguistic analysis material diverse in topic and style, and at Web scale. We investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity. These properties are found to be significantly different across the examined cohorts, which suggest discriminative features for a number of useful classification tasks. We build binary classifiers for old versus young bloggers, social versus solo bloggers, and happy versus sad posts with high performance. Analysis of discriminative features shows that age turns upon choice of topic, whereas sentiment orientation is evidenced by linguistic style. Good prediction is achieved for social connectivity using topic and linguistic features, leaving tagged mood a modest role in all classifications.

Applying SPSE model to the comprehension of text organization by EFL learners

In order to test the effect of discourse organization on reading comprehension, two expository texts having an SPSE (situation- problem solution-evaluation) pattern were adminestered to a group of 30 undergraduate EFL students from Shahid Chamran University of Ahwaz who had been screened from among 100 students. These students had scored 60 and over from a language proficiency test having 75 items. The results of the study confirmed that the subjects had relatively more difficulty in recalling the evaluation and the solution sections, and in particular the details of 'solution', than other sections of the expository texts. It is concluded that in addition to language proficiency, other factors such as voice and cognition which contribute to the organization of text and hence to the comprehensibility of it are essential.

Text-dependent speaker recognition using the Fuzzy ARTMAP neural network

Speaker recognition is the process of automatically recognizing the speaker by analyzing individual information contained in the speech waves. In this paper, we discuss the development of an intelligent system for text-dependent speaker recognition. The system comprises two main modules, a wavelet-based signal-processing module for feature extraction of speech waves, and an artificial-neural-network-based classifier module to identify and categorize the speakers. Wavelet is used in de-noising and in compressing the speech signals. The wavelet family that we used is the Daubechies Wavelets. After extracting the necessary features from the speech waves, the features were then fed to a neural-network-based classifier to identify the speakers. We have implemented the Fuzzy ARTMAP (FAM) network in the classifier module to categorize the de-noised and compressed signals. The proposed intelligent learning system has been applied to a case study of text-dependent speaker recognition problem.

Text-dependent speaker recognition using wavelets and neural networks

An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

Process drama and digital games as text and action in virtual worlds : developing new literacies in school

This article explores the intersections between drama and digital gaming and the educational possibilities for literacy of both. The article draws on a model for the educational uses of digital gaming and three case studies from the Australian Research Council funded three and a half year project, Literacy in the digital world of the twenty first century: Learning from computer games. This model theorises the scope of the possibilities for literacy outcomes from the usage of computer games. The article describes how the model works, and then applies the model to drama education, specifying some new ways of thinking about the literacy outcomes from drama education. Process drama is theorised as the creation of text-in-action.

Business marketing : text and cases

Produced for undergraduate unit MMK368 (Business marketing) offered by the Faculty of Business and Law's Bowater School of Management and Marketing in Deakin University's flexible learning program.