Noise sensitivity medical, psychological and genetic aspects

Noise can be defined as unwanted sound. It may adversely affect the health and well-being of individuals. Noise sensitivity is a personality trait covering attitudes towards noise in general and a predictor of noise annoyance. Noise sensitive individuals are more affected by noise than less sensitive individuals. The determinants and characteristics related to noise sensitivity are rather poorly known. The risk of health effects caused by noise can be hypothesized to be higher for noise sensitive individuals compared to those who are not noise sensitive. A cardiovascular disease may be an example of outcomes. The general aim of the present study was to investigate the association of noise sensitivity with specific somatic and psychological factors, including the genetic component of noise sensitivity, and the association of noise sensitivity with mortality. The study was based on the Finnish Twin Cohort of same-sex twin pairs born before 1958. In 1988 a questionnaire was sent to twin pairs discordant for hypertension. 1495 individuals (688 men, 807 women) aged 31 88 years replied, including 573 twin pairs. 218 of the subjects lived in the Helsinki Metropolitan Area. Self-reported noise sensitivity, lifetime noise exposure and hypertension were obtained from the questionnaire study in 1988 and other somatic and psychological factors from the questionnaire study in 1981 for the same individuals. In addition, noise map information (1988 1992) from the Helsinki Metropolitan Area and mortality follow-up 1989 2003 were used. To evaluate the stability and validity of noise sensitivity, a new questionnaire was sent in 2002 to a sample of the subjects who had replied to the 1988 questionnaire. Of all subjects who had answered the question on noise sensitivity, 38 % were noise sensitive. Noise sensitivity was independent of noise exposure levels indicated in noise maps. Subjects with high noise sensitivity reported more transportation noise exposure than subjects with low noise sensitivity. Noise sensitive subjects reported transportation noise exposure outside the environmental noise map areas almost twice as often as non-sensitive subjects. Noise sensitivity was associated with hypertension, emphysema, use of psychotropic drugs, smoking, stress and hostility, even when lifetime noise exposure was adjusted for. Monozygotic twin pairs were more similar with regards noise sensitivity than dizygotic twin pairs, and quantitative genetic modelling indicated significant familiality. The best fitting genetic model provided an estimate of heritability of 36 %. Follow-up of subjects in the case-control study showed that cardiovascular mortality was significantly increased among noise sensitive women, but not among men. For coronary heart mortality the interaction of noise sensitivity and lifetime noise exposure was statistically significant in women. In conclusion, noise sensitivity has both somatic and psychological components. It does aggregate in families and probably has a genetic component. Noise sensitivity may be a risk factor for cardiovascular mortality in women.

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.