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Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing(More)
We present a preliminary study of several parser adaptation techniques evaluated on the GENIA corpus of MEDLINE abstracts [1, 2]. We begin by observing that the Penn Treebank (PTB) is lexically impoverished when measured on various genres of scientific and technical writing, and that this significantly impacts parse accuracy. To resolve this without(More)
We identify a set of prosodic cues for parsing conversational speech and show how such features can be effectively incorporated into a statistical parsing model. On the Switchboard corpus of conversational speech, the system achieves improved parse accuracy over a state-of-the-art system which uses only lexical and syntactic features. Since removal of edit(More)
A grammatical method of combining two kinds of speech repair cues is presented. One cue, prosodic disjuncture, is detected by a decision tree-based ensemble clas-sifier that uses acoustic cues to identify where normal prosody seems to be interrupted (Lickley, 1996). The other cue, syntactic parallelism, codifies the expectation that repairs continue a(More)
While wireless sensor networks offer new capabilities, there are a number of issues that hinder their deployment in practice. We argue that robotics can solve or greatly reduce the impact of many of these issues. Our hypothesis has been tested in the context of an autonomous system to care for houseplants that we have deployed in our office environment.(More)
The advent of crowdsourcing has created a variety of new opportunities for improving upon traditional methods of data collection and annotation. This in turn has created intriguing new opportunities for data-driven machine learning (ML). Convenient access to crowd workers for simple data collection has further generalized to leveraging more arbitrary(More)
We investigate human factors involved in designing effective Human Intelligence Tasks (HITs) for Amazon's Mechanical Turk 1. In particular, we assess document relevance to search queries via MTurk in order to evaluate search engine accuracy. Our study varies four human factors and measures resulting experimental outcomes of cost, time, and accuracy of the(More)
While many statistical consensus methods now exist, relatively little comparative benchmarking and integration of techniques has made it increasingly difficult to determine the current state-of-the-art, to evaluate the relative benefit of new methods, to understand where specific problems merit greater attention, and to measure field progress over time. To(More)
We present a new learning to rank framework for estimating context-sensitive term weights without use of feedback. Specifically, knowledge of effective term weights on past queries is used to estimate term weights for new queries. This generalization is achieved by introducing secondary features correlated with term weights and applying regression to(More)