Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.

Azione destabilizzante e impegno politico. Le figure femminili tra il giallo e il nero Italiani dal 1980 a oggi

My thesis analyses female figures in Italian crime fiction since 1980, from narratological, sociological and gender-studies perspectives. It considers the narrative structure of the giallo and the noir, taking into account the difficulties, particularly in Italy, of establishing what noir fiction is and if/how it is possible to frame it in a specific narrative scheme. This discourse connects to the extraordinary success of Italian giallo and noir fiction over the past fifteen-twenty years. In this scenario, I examine the place of female writers in relation to this phenomenon, especially since the 1980s: in terms of the level of visibility/acceptance of their work, in a narrative space traditionally considered as a male territory, and with regard to their writings, which introduce different narrative perspectives. Specifically, I consider selected texts by leading female Italian writers, in which female elements (writers/characters) become destabilizing factor both on the narrative level, by undermining the schemes of ‘formulaic’ fiction, and on the political one, through their implicit potential (as women) for disrupting the rigid models inherited from processes of social conditioning and from political structures. In terms of gender identities, such texts offer scope to question conventional social constructions of the subject: by empowering the female narrative voice and by embodying it in characters (investigators/killers) traditionally personified by male figures. These texts offer themselves as a critical space where, potentially, readers can rethink static gendered relationships and stereotypical symbolical categories.

Uncertainty visualization in 3D scalar data

One problem in most three-dimensional (3D) scalar data visualization techniques is that they often overlook to depict uncertainty that comes with the 3D scalar data and thus fail to faithfully present the 3D scalar data and have risks which may mislead users’ interpretations, conclusions or even decisions. Therefore this thesis focuses on the study of uncertainty visualization in 3D scalar data and we seek to create better uncertainty visualization techniques, as well as to find out the advantages/disadvantages of those state-of-the-art uncertainty visualization techniques. To do this, we address three specific hypotheses: (1) the proposed Texture uncertainty visualization technique enables users to better identify scalar/error data, and provides reduced visual overload and more appropriate brightness than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (2) The proposed Linked Views and Interactive Specification (LVIS) uncertainty visualization technique enables users to better search max/min scalar and error data than four state-of-the-art uncertainty visualization techniques, as demonstrated using a perceptual effectiveness user study. (3) The proposed Probabilistic Query uncertainty visualization technique, in comparison to traditional Direct Volume Rendering (DVR) methods, enables radiologists/physicians to better identify possible alternative renderings relevant to a diagnosis and the classification probabilities associated to the materials appeared on these renderings; this leads to improved decision support for diagnosis, as demonstrated in the domain of medical imaging. For each hypothesis, we test it by following/implementing a unified framework that consists of three main steps: the first main step is uncertainty data modeling, which clearly defines and generates certainty types of uncertainty associated to given 3D scalar data. The second main step is uncertainty visualization, which transforms the 3D scalar data and their associated uncertainty generated from the first main step into two-dimensional (2D) images for insight, interpretation or communication. The third main step is evaluation, which transforms the 2D images generated from the second main step into quantitative scores according to specific user tasks, and statistically analyzes the scores. As a result, the quality of each uncertainty visualization technique is determined.