Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise. In this thesis, we propose a framework for automatically routing a newly posted question to the best suited expert. The purpose of this framework is to decrease the waiting time for a personal response. We also investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a set of best experts. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.

Thesis (doctoral)--Georg-August-Universität zu Göttingen.

The obligation of the State to ensure children have access to healthcare is surprisingly contentious with Western capitalism demanding open markets free from interference. Such a view holds healthcare services as a commodity to be traded. A ‘right’ to health is only a goal to many, not a tangible guarantee States can rationally be expected to ensure because of the enormous costs and the difficulties presented to a court in adjudicating this right. On this view it is impossible for a child to have a legal right to access healthcare. This thesis combats such arguments. The obligation of the State is discussed from a moral standpoint, finding that the child’s right to health must be a State and a global obligation in any just society. Pragmatic discussion addresses the problem of legalising the obligation and showing the right can be a tangible guarantee. This is done through two paradigms: firstly, by looking at current international law and its implementation; and secondly, by looking at countries with a right to healthcare in their written constitution and adjudication of such a right. This combats the legal right arguments as well as provides lessons that international law can learn from. This thesis contributes to discussion around the effective enforcement and implementation of human rights, especially economic, social and cultural rights. It does this by examining the scope of a child’s right to health, and arguing for a moral obligation for its provision, as well as more pragmatic discussion on how to enforce such rights and adjudicate them to make them worth more than words on paper. The final chapter brings together various proposals for tackling the global challenge to ensure every child in the world has access to basic minimum healthcare.

This thesis analyses the development, methodology, and results of a quantitative study of the decision-making of the England and Wales Court of Appeal (Criminal Division). The Court of Appeal plays an important constitutional role, and the impartiality of the judges is central to its legitimacy. Drawing upon research from the Empirical Legal Studies (ELS) research community, this thesis explores the question of the Court of Appeal’s impartiality. As an incomplete measurement of impartiality, a sample of the Court of Appeal’s decisions has been analysed. A dataset of all murder and rape appeals against conviction decided between 2006 and 2010 has been created. A range of factual, demographic, and legal variables have been collected from each of these 472 appeals against conviction, utilising quantitative content analysis. It has been determined, utilising binary logistic regression analysis, whether the variables under analysis are predictors of the outcome of appeals against conviction. Almost all of the variables analysed showed only a limited ability to predict the outcomes of appeals. Moreover, this study finds support for the legal model of judicial decision-making. A variable designed to capture impartial decision-making had the strongest association with the outcome of appeals. However, a small number of factual and demographic variables are shown to be predictors of outcomes. There is insufficient evidence to doubt the impartiality of the Court of Appeal, but the emergence of these patterns in the data warrants further investigation. This conclusion is important to users and observers of the Court, to whom the impartiality, and so legitimacy, of the Court’s decision-making is essential.

Unstructured textual data is rapidly growing and practitioners from diverse disciplines are expe- riencing a need to structure this massive amount of data. Topic modeling is one of the most used techniques for analyzing and understanding the latent structure of large text collections. Probabilistic graphical models are the main building block behind topic modeling and they are used to express assumptions about the latent structure of complex data. This dissertation address four problems related to drawing structure from high dimensional data and improving the text mining process. Studying the ebb and flow of ideas during critical events, e.g. an epidemic, is very important to understanding the reporting or coverage around the event or the impact of the event on the society. This can be accomplished by capturing the dynamic evolution of topics underlying a text corpora. We propose an approach to this problem by identifying segment boundaries that detect significant shifts of topic coverage. In order to identify segment boundaries, we embed a temporal segmentation algorithm around a topic modeling algorithm to capture such significant shifts of coverage. A key advantage of our approach is that it integrates with existing topic modeling algorithms in a transparent manner; thus, more sophisticated algorithms can be readily plugged in as research in topic modeling evolves. We apply this algorithm to studying data from the iNeighbors system, and apply our algorithm to six neighborhoods (three economically advantaged and three economically disadvantaged) to evaluate differences in conversations for statistical significance. Our findings suggest that social technologies may afford opportunities for democratic engagement in contexts that are otherwise less likely to support opportunities for deliberation and participatory democracy. We also examine the progression in coverage of historical newspapers about the 1918 influenza epidemic by applying our algorithm on the Washington Times archives. The algorithm is successful in identifying important qualitative features of news coverage of the pandemic. Visually convincing results of data mining algorithms and models is crucial to analyzing and driving conclusions from the algorithms. We develop ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets. ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur. This algorithm detects not only the clustering and associations of keywords in a time period, but also their convergence into topics (groups of keywords) that may later diverge into new groups. The visual representation of ThemeDelta uses sinuous, variable-width lines to show this evolution on a timeline, utilizing color for categories, and line width for keyword strength. We demonstrate how interaction with ThemeDelta helps capture the rise and fall of topics by analyzing archives of historical newspapers, of U.S. presidential campaign speeches, and of social messages collected through iNeighbors. ThemeDelta is evaluated using a qualitative expert user study involving three researchers from rhetoric and history using the historical newspapers corpus. Time and location are key parameters in any event; neglecting them while discovering topics from a collection of documents results in missing valuable information. We propose a dynamic spatial topic model (DSTM), a true spatio-temporal model that enables disaggregating a corpus's coverage into location-based reporting, and understanding how such coverage varies over time. DSTM naturally generalizes traditional spatial and temporal topic models so that many existing formalisms can be viewed as special cases of DSTM. We demonstrate a successful application of DSTM to multiple newspapers from the Chronicling America repository. We demonstrate how our approach helps uncover key differences in the coverage of the flu as it spread through the nation, and provide possible explanations for such differences. Major events that can change the flow of people's lives are important to predict, especially when we have powerful models and sufficient data available at our fingertips. The problem of embedding the DSTM in a predictive setting is the last part of this dissertation. To predict events and their locations across time, we present a predictive dynamic spatial topic model that can predict future topics and their locations from unseen documents. We showed the applicability of our proposed approach by applying it on streaming tweets from Latin America. The prediction approach was successful in identify major events and their locations. / Ph. D.

The paper explicates the notions of topic, contrastive topic, and focus as used in the analysis of Hungarian. Based on distributional criteria, topic and focus are claimed to represent distinct structural positions in the left periphery of the Hungarian sentence, associated with logical rather than discourse functions. The topic is interpreted as the logical subject of predication. The focus is analyzed as a derived main predicate, specifying the referential content of the set denoted by the backgrounded post-focus section of the sentence. The exhaustivity associated with the focus and the existential presupposition associated with the background are shown to be properties following from their specificational predication relation.

With the growth of digital content industry, publishers start to provide online services for ebook search, reading and downloading. Users can access to online resources from anywhere, any place with laptop or mobile devices at any time. Nowadays more and more libraries have purchased ebooks as an important part of the library collection. To access the online resources users can link directly to publisher's ebook portal or via the OPAC system. Compared to the library circulation process, ebooks are more convenient to patrons and improve the utilization of library online resources. There are various kinds of ebooks available in the market, so libraries have to focus their investment on the most valuable online resources. Usage statistics report plays an important role in providing valuable information to libraries. It is usually based on the standard of COUNTER to generate the statistic reports, although it provides when and where users access to specific ebooks, it fails show the general topics and how they change. In this study, we introduce a post process method to weighting the LDA topic model via the usage statistic report to emphasize the changes of topic and compare it to the classification method and subject heading method in the bibliographic, namely LCC and LCSH respectively. The result show that weighted topic model significantly affect the ranking of topics, and the topic model are independent from the classification method and the subject heading method in the bibliographic record.

The electronics newspaper becomes a main source for online news readers. When facing the numerous stories, news readers need some supports in order to review a topic in short time. Due to previous researches in TDT (Topic Detection and Tracking) only considering how to identify events and present the results with news titles and keywords, a summarized text to present event evolution is necessary for general news readers to retrospect events under a news topic. This thesis proposes a topic retrospection process and implements the SToRe system that identifies various events under a new topic and constructs the relationship to compose a summary which gives readers the sketch of event evolution in a topic. It consists of three main functions: event identification, main storyline construction and storyline-based summarization. The constructed main storyline can remove the irrelevant events and present a main theme. The summarization extracts the representative sentences and takes the main theme as the template to compose summary. The summarization not only provides enough information to comprehend the development of a topic, but also can be an index to help readers to find more detailed information. A lab experiment is conducted to evaluate the SToRe system in the question-and-answer (Q&A) setting. From the experimental results, the SToRe system can help news readers more effectively and efficiently to capture the development of a topic.

Children's learning of language-universal and language-specific principles of argument representation was the topic under investigation in the three studies comprising this thesis. Another objective was to investigate whether a discourse-pragmatic approach could be employed to explain children's patterns of argument omission and production, developmentally and crosslinguistically. To answer these questions, referential choice in the spontaneous language of monolingual English-speaking and monolingual Japanese-speaking children and their mothers was developmentally investigated whereby a sentence argument's morphological form (null, pronominal, lexical), referential status (given, new), and syntactic location (transitive subject, transitive object, intransitive subject) were systematically analysed. The first and second studies revealed that neither the English-speaking nor the Japanese-speaking children showed sensitivity to the referential distinction between given and new information early on in development (at 21 months of age). The English-speaking children mastered English-specific referential conventions between MLU 2.00 and 3.99 (between 24 and 32 months) and employed non-linguistic pragmatic correlates to supplement unconventional argument use from as early as MLU 1.00 (between 21 and 23 months). By contrast, the Japanese-speaking children showed unconventional referential choices as late as MLU 4.00 (between 33 and 36 months), as well as inconsistent use of non-linguistic pragmatic correlates. The third study revealed that, although language-specific differences were observed, neither group of children violated any of the four Preferred Argument Structure (PAS) constraints: The children avoided using more than one new or lexical argument per transitive clause and avoided casting new or lexical arguments as transitive subjects. However, evidence of sensitivity to PAS strategies from early on in development was inconclusive because the children omitted most sentence arguments at the beginning of speech production. Finally, all three studies revealed that children's referential choices that were inconsistent with expected discourse-pragmatic principles reflected similar patterns observed in parental input. Altogether, this set of studies led to the following general conclusions regarding the learning of argument representation and distribution in syntax: (1) a discourse-pragmatic approach can explain language-universal features of argument omission and production in child language and (2) language-specific strategies are learned via parental input.

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