The subject of this thesis covers a considerably varied multidisciplinary area which needs to be addressed to be able to achieve a text-to-speech synthesis system of high quality, in any language. This is the first time that such a system has been built for Maltese, and therefore, there was the additional problem of no computerised sources or corpora. However many problems and much of the system designs are common to all languages. This thesis focuses on two general problems. The first is that of automatic labelling of phonemic data, since this is crucial for the setting up of Maltese speech corpora, which in turn can be used to improve the system. A novel way of achieving such automatic segmentation was investigated. This uses a mixed parameter model with maximum likelihood training of the first derivative of the features across a set of phonetic class boundaries. It was found that this gives good results even for continuous speech provided that a phonemic labelling of the text is available. A second general problem is that of segment concatenation, since the end and beginning of subsequent diphones can have mismatches in amplitude, frequency, phase and spectral envelope. The use of-intermediate frames, build up from the last and first frames of two concatenated diphones, to achieve a smoother continuity was analysed. The analysis was done both in time and in frequency. The use of wavelet theory for the separation of the spectral envelope from the excitation was also investigated. The linguistic system modules have been built for this thesis. In particular a rule based grapheme to phoneme conversion system that is serial and not hierarchical was developed. The morphological analysis required the design of a system which allowed two dissimilar lexical structures, (semitic and romance) to be integrated into one overall morphological analyser. Appendices at the back are included with detailed rules of the linguistic modules developed. The present system, while giving satisfactory intelligibility, with capability of modifying duration, does not include as yet a prosodic module.

Following the loss of heavy, manufacturing industry in many industrial areas in the 1970s and 1980s, tourism has featured extensively in urban and wateriront regeneration policy because of its ability to generate substantial economic benefits to destination communities. There is now an extensive literature covering urban tourism and dockland regeneration, but visitors' perceptions of urban waterfront destinations and their on-site behaviour and d experience remain largely unexplored. Additionally, whilst there is now a substantial body of literature relating to tourism's economic impact at the macro level, less is known about tourism expenditure at destination and sub-destination levels.

Master of Science / Department of Computer Science / William H. Hsu / This work describes a comparative study of empirical methods for categorization of new articles within text corpora: unsupervised learning for an unlabeled corpus of text documents and supervised learning for hand-labeled corpus. The goal of text categorization is to organize natural language (i.e. human language) documents into categories that are either predefined or that are inherently grouped by similar meaning. The first approach, automatic classification of texts, can be handy when handling massive amounts of data and has many applications such as automated indexing of scientific articles, spam filtering, classification of news articles etc. Classification using supervised or semi-supervised inductive learning involves labeled data, which can be expensive to acquire and may require semantically deep understanding of the meaning of texts. The second approach falls under the general rubric of document clustering, based on the statistical distribution and co-occurrence of words in a full-text document. Developing a full pipeline for document categorization draws on methods from information retrieval (IR), natural language processing (NLP), and machine learning (ML). In this project, experiments are conducted on two text corpora: news aggregator data, which contains news headlines collected from a web aggregator and a news data set consisting of original news articles from the British Broadcasting Corporation (BBC). First, the training data is developed from these corpora. Next, common types of supervised classifiers, such as linear, Bayesian, ensemble models and support vector machines (SVM) are trained, on the labelled data and the trained classification models are used to predict the category of an article, given the related text. The results obtained are analyzed and compared to determine the best performing model. Then, two unsupervised learning techniques – k-means and Latent Dirichlet Allocation (LDA) are applied to obtain clusters of data points. k-means separates the documents into disjoint clusters of similar news. Additionally, LDA was used, which treats documents as a mixture of topics, to find latent topics in text. Finally, visualizations of the results are produced for evaluation: to allow qualitative assessment of cluster separation in the case of unsupervised learning, or to understand the confusion matrix for the supervised classification task by heat map visualization as well as precision, recall, and other holistic metrics. From an application standpoint, the unsupervised techniques applied can be used to find news that are similar in content and can be categorized under a specific topic.

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Communal coping—a process in which romantic partners view a problem or stressor as "ours" rather than "yours" or "mine" and engage in collaborative problem solving to address it —has emerged as an important predictor of health and treatment outcomes. This study investigated communal coping as a theoretically derived and empirically supported intervention target within two couple-focused interventions for health problems: Family Systems Therapy for problematic alcohol use and Family Consultation for health-compromised smoking. With a combined sample of 56 couples (37 alcohol, 21 smoking), this study investigated within-session changes in communal coping—indexed via observable, communal coping behaviors and first-person plural pronoun use (we-talk)—prior to and following therapist implementation of specific solution-focused therapy techniques that aimed to promote communal coping in the couples during a target therapy session. Teams of trained raters observed the target therapy sessions and made independent ratings of couple communal coping behaviors and therapist adherence. Pronoun measures for each partner were obtained via computerized text analysis from transcripts of partners' speech during the target therapy sessions. Both patients and spouses showed increases in communal coping behavior and we-talk from a "baseline" problem-focused therapy block to the "active" solution-focused therapy block. In addition, exploratory analyses revealed that several couple and therapist characteristics, as well as specific solution-focused techniques were associated with within-session changes in communal coping. Findings from this study identify communal coping as a client change process and solution-focused therapy techniques as a therapist change process within the two interventions, and demonstrate successful engagement of communal coping as a therapeutic target.

The patriarchal legal system and the socio-cultural institutions of the Islamic Republic of Iran (IRI) relegate Iranian women to second-class citizens. Yet, Canadian feminist texts such as Margaret Atwood's The Handmaid's Tale (1985) and Carol Shields's Unless (2002) have been translated into Persian, in 2003 and 2005 respectively. Moreover, they circulate freely and are found in Iran’s National Library. This seeming discrepancy needs a systemic and contextually-based explanation. Four questions guide my dissertation: What happens to the texts as they cross the cultural boundaries into the receiving society? Specifically, which features of feminist texts are most vulnerable to censorial interventions and what does that reveal about the interplay of the hegemonic theocratic-patriarchy and translation? Finally, how is the Persian translation of feminist texts even possible, given Iran’s legal, political and socio-cultural antagonism toward women’s autonomy? In other words, what factors mitigate such translations? To answer these questions, I outline the legal representation of women in the legal discourse and the socio-cultural attitudes towards women’s rights in Iran subsequent to the (1906-1911) Constitutional Revolution and the 1979 Revolution, which led to an Islamist government. I examine the impacts of the IRI’s androcentric legislations on women’s rights, and the censorship mechanisms on Persian and imported feminist literature. I explore the types and extent of resistance to censorship, and I study the representation of women in school textbooks, cinema and Persian literature to analyze the impact that the interaction between the legal discourse, censorship and resistance has on cultural products. I conduct a comparative text analysis using theories of feminist linguistics and descriptive translation studies (Toury 1995; Cameron 1985, 1995) to investigate the extent to which patriarchal mechanisms influence the translation of the two novels. The goal is to determine how the legal and socio-cultural discourses of the target society affect the form and meaning of the translation, and to identify translation strategies that undermine the very features that make a novel female-centric. I demonstrate how these translation strategies consistently produce target texts that conform to the state-sponsored patriarchal agenda, and synchronize with the gender values and norms of the IRI.

In this paper we intend to reveal media image of former president of Iraq, Saddam Hussein as it has been constructed for six months before the war in 2003 has started. Research material came from Newsweek International news magazine. Text was decyphered using semiotic methods set in the theoretical framework based on Marxism and some of its followers. We found the representation of a "dictator" an ideological narrative serving needs of dominant ideology.

Abstract Due to the vast amount of information we are faced with, summarization has become a critical necessity of everyday human life. Given that a large fraction of the electronic documents available online and elsewhere consist of short texts such as Web pages, news articles, scientific reports, and others, the focus of natural language processing techniques to date has been on the automation of methods targeting short documents. We are witnessing however a change: an increasingly larger number of books become available in electronic format. This means that the need for language processing techniques able to handle very large documents such as books is becoming increasingly important. This thesis addresses the problem of summarization of novels, which are long and complex literary narratives. While there is a significant body of research that has been carried out on the task of automatic text summarization, most of this work has been concerned with the summarization of short documents, with a particular focus on news stories. However, novels are different in both length and genre, and consequently different summarization techniques are required. This thesis attempts to close this gap by analyzing a new domain for summarization, and by building unsupervised and supervised systems that effectively take into account the properties of long documents, and outperform the traditional extractive summarization systems typically addressing news genre.

User generated reviews, like those found on Yelp and Amazon, have become important reference material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process. In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud. We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques. [Permission email from J. Huang removed at his request. GMc March 11, 2014] / Master of Science

German) investigation into specific textual elements of academic articles – those phrases or passages that we call ‘text commentaries’ or, more accurately and using speech act terminology, ‘text commenting speech actions’. Our empirical investigation is based on two corpora – a German corpus, comprising at present 19 research articles, and an English corpus with 17 research articles. The articles have been taken from academic journals of many disciplines.