We introduce a new, powerful class of text proximity queries: find an instance of a given "answer type" (person, place, distance) near "selector" tokens matching given literals or satisfying given ground predicates. An example query is type=distance NEAR Hamburg Munich. Nearness is defined as a flexible, trainable parameterized aggregation function of the selectors, their frequency in the corpus, and their distance from the candidate answer. Such queries provide a key data reduction step for information extraction, data integration, question answering, and other text-processing applications. We describe the architecture of a next-generation information retrieval engine for such applications, and investigate two key technical problems faced in building it. First, we propose a new algorithm that estimates a scoring function from past logs of queries and answer spans. Plugging the scoring function into the query processor gives high accuracy: typically, an answer is found at rank 2-4. Second, we exploit the skew in the distribution over types seen in query logs to optimize the space required by the new index structures required by our system. Extensive performance studies with a 10GB, 2-million document TREC corpus and several hundred TREC queries show both the accuracy and the efficiency of our system. From an initial 4.3GB index using 18,000 types from WordNet, we can discard 88% of the space, while inflating query times by a factor of only 1.9. Our final index overhead is only 20% of the total index space needed.

Question classification is an important step in factual question answering (QA) and other dialog systems. Several attempts have been made to apply statistical machine learning approaches, including Support Vector Machines (SVMs) with sophisticated features and kernels. Curiously, the payoff beyond a simple bag-ofwords representation has been small. We show that most questions reveal their class through a short contiguous token subsequence, which we call its informer span. Perfect knowledge of informer spans can enhance accuracy from 79.4% to 88% using linear SVMs on standard benchmarks. In contrast, standard heuristics based on shallow pattern-matching give only a 3% improvement, showing that the notion of an informer is non-trivial. Using a novel multi-resolution encoding of the question's parse tree, we induce a Conditional Random Field (CRF) to identify informer spans with about 85% accuracy. Then we build a meta-classifier using a linear SVM on the CRF output, enhancing accuracy to 86.2%, which is better than all published numbers.

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CDK is a predisposing factor for infectious keratitis. Treatment should be considered for advanced and nodular lesions, even if they are peripheral, to prevent infectious keratitis.