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This paper describes the Question Answering System constructed during a one semester graduatelevel course on Natural Language Processing (NLP). We h ypothesized that by using a combination of syntactic and semantic features and machine learning techniques, we could improve the accuracy of question answering on the test set of the Remedia corpus over the reported levels. The approach, although novel, was not entirely successful in the time frame of the course.

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We propose a stochastic context-free grammar model whose structure can alternatively be viewed as a graphical model, and use it to model time series. We use the inside-outside algorithm to estimate the model parameters. We assume that the time series is a nite-order Markov process generated by our model, and develop an algorithm to forecast the conditional variance of the process. We use this algorithm to forecast the volatility of the S&P 500 index, achieving results that outperform both standard and more recent approaches.