This paper considers the problem of automatic assessment of local coherence. We present a novel entity-based representation of discourse which is inspired by Centering Theory and can be computed automatically from raw text. We view coherence assessment as a ranking learning problem and show that the proposed discourse representation supports the effective learning of a ranking function. Our experiments demonstrate that the induced model achieves significantly higher accuracy than a state-of-the-art coherence model. 1

The principles of readability are in every style manual. Readability formulas are in every word processor. What is missing is the research and theory on which they stand.

• In the HARD track, we developed document metadata to correspond to query metadata requirements; implemented clarification forms based on query expansion, passage retrieval, and clustering; and retrieved variable length passages deemed most likely to be relevant. This work is discussed at length in Section 1. • In the QA track, we focused on retrieving passages that were likely to contain the answer to the question. 1 HARD track 1.1 Overview The goal of the High Accuracy Retrieval from Documents track was to explore techniques for improving the accuracy of the top-ranked documents in response to a query. We participated in all three aspects of the problem: • We mapped query metadata values to document metadata values that we assigned. We then adjusted the ranking of documents depending on whether their metadata matched the query metadata. • We generated clarification forms to tease more information out of the searcher. We tried several types of clarification forms, including providing a list of keywords that might appear in relevant documents, a list of top-ranking clusters that might contain relevant documents, and a list of passages that might appear in relevant documents. • We explored passage-level retrieval of documents to see if we could pinpoint the relevant portions of documents. In the final analysis, all runs using metadata or clarification forms failed to outperform our best baseline run. We interpret

This document is a first step towards the construction of a maintainability prediction model. It identifies a set of software measures based on correlations published in empirical studies.