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OBJECTIVE To determine the relationship of brain infarction to the clinical expression of Alzheimer disease (AD). DESIGN Cognitive function and the prevalence of dementia were determined for participants in the Nun Study who later died. At autopsy, lacunar and larger brain infarcts were identified, and senile plaques and neurofibrillary tangles in the(More)
OBJECTIVES This study investigated the role of low normal cognitive function in the subsequent loss of independence in activities of daily living. METHODS Of the 678 elderly nuns who-completed cognitive and physical function assessments in 1992/93, 575 were reassessed in 1993/94. Mini-Mental State Examination scores were divided into three categories and(More)
We investigated the relationship of self-rated function (i.e., the ability to take care of oneself) and self-rated health to concurrent functional ability, functional decline, and mortality in participants in the Nun Study, a longitudinal study of aging and Alzheimer's disease. A total of 629 of the 678 study participants self-rated their function and(More)
Being able to quantify the semantic similarity between two texts is important for many practical applications. SemantiKLUE combines unsupervised and supervised techniques into a robust system for measuring semantic similarity. At the core of the system is a word-to-word alignment of two texts using a maximum weight matching algorithm. The system(More)
OBJECTIVES Self-rated function is a new global measure. Previous findings suggest that self-rated function predicts future functional decline and is strongly associated with all-cause mortality. We hypothesized that the strength of the relationship of self-rated function to all-cause mortality was in part due to functional decline, such as would occur with(More)
This paper describes our approach to the SemEval-2013 task on " Sentiment Analysis in Twitter ". We use simple bag-of-words models , a freely available sentiment dictionary automatically extended with distributionally similar terms, as well as lists of emoticons and inter-net slang abbreviations in conjunction with fast and robust machine learning(More)
This paper describes our system entered for the *SEM 2013 shared task on Semantic Textual Similarity (STS). We focus on the core task of predicting the semantic textual similarity of sentence pairs. The current system utilizes machine learning techniques trained on semantic similarity ratings from the *SEM 2012 shared task; it achieved rank 20 out of 90(More)