Shooting from the Wild Zone : A Study of the Chicana Art Photographers Laura Aguilar, Celia Álvarez Muñoz, Delilah Montoya, and Kathy Vargas

The dissertation analyzes and elaborates upon the changing map of U.S. ethno-racial formation from the vantage point of North American Studies, multi-disciplinary cultural studies, and the criticism of visual culture. The focus is on four contemporary Mexican American (Chicana) women photographers, whose art production is discussed, on the one hand, in the context of the Euro-American history of photographic genres and, on the other hand, in the context of so-called decolonizing cultural and academic discourses produced by Mexican Americans themselves. The manuscript consists of two parts. Part I outlines the theoretical and methodological domain of the study, positioning it in the interstices of American studies, European postmodern criticism, postcolonial feminist theory, and the theories of visual culture, particularly of art photography. In addition, the main issues and paradigms of Chicano Studies (Mexican American ethnic studies) are introduced. Part II consists of seven essays, each of which discusses rather independently a particular photographic work or a series of photographs, formulating and defending arguments about their meaning, position in the history of photographic genres, and their cultural and socio-political significance. The study closes with a discussion about ethno-racial identity formation and the role of Chicana photography therein - in embodying and reproducing new subjectivities, alternative categories of knowledge, and open ended historical narratives. It is argued that, symbolically, the "Wild Zone" of gendered and race-specific knowledge becomes associated with the body of the mother, a recurrent image in Chicana art works under discussion. Embedded in this image, the construction of an alternative notion of a family thus articulates the parameters of a matrifocal ethno-racial community unified by the proliferation of differences rather than by conformities typical of nationalistic ideologies. While focusing on art photography, the study as a whole simultaneously constructs, from a European vantage point, a "thick" description of Mexican American history, identities, communities, cultural practices, and self-representations about which very little is known in Finland.

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.