We propose a method for automatically identifying rhetorical relations. We use supervised machine learning but exploit cue phrases to automatically extract and label training data. Our models draw on a variety of linguistic cues to distinguish between the relations. We show that these feature-rich models outperform the previously suggested bigram models by more than 20%, at least for small trai...

We review various K-theory classification conjectures in string theory. Sen conjecture based proposals classify D-brane trajectories in backgrounds with no H flux, while Freed-Witten anomaly based proposals classify conserved RR charges and magnetic RR fluxes in topologically time-independent backgrounds. In exactly solvable CFTs a classification of well-defined boundary states implies that the...

It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verbnoun pair taken from a predefined ontology describing typical “email speech acts”. We demonstrate that, although this categorization problem is quite dif...

How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a natural stochastic model of the learners, modeling them as randomly switching among hypotheses based on observed feedback. We then develop STRICT...

Previous Internet topology studies mostly focused on ASand router-level topologies [1,2,4,8,9], providing insight into AS relationships and interdomain routing. However, relatively little is known today about the demographics of Internet edge hosts and the use of the IPv4 address space. Since the transition to classless routing [3], external observers have only limited view into how IP address ...

We have produced an ontology specifying a model of computer attack. Our ontology is based upon an analysis of over 4000 classes of computer intrusions and their corresponding attack strategies and is categorised according to system component targeted, means of attack, consequence of attack and location of attacker. We argue that any taxonomic characteristics used to define a computer attack be ...

FIVE-AND-FORTY years have elapsed since Ehrenberg published his classification of the Rotifera, and his system still holds its ground. The mere statement of the fact is high praise; for what have not classifiers altered, and attempted to alter, during the last half century ? Not that his classification has escaped challenge. It was sharply criticised in the ' Histoire naturelle des Zoophytes*by...

Patients of chronic gastritis should be investigated with gastric mucosal biopsy, parietal cell antibody, intrinsic factor antibody, Helicobacter pylori antibody, urea breath test or faecal antigen test for Helicobacter pylori, to accurately classify them. The results of these tests will indicate Helicobacter pylori infection (present or past), the role of hereditary factor (intrinsic factor an...

Automated laboratories analyze thousands of blood tests every day. Some samples can become contaminated by improper collection, producing incorrect results. Identification of such samples is critical and requires a labor intensive expert review. In our previous work, we demonstrated a useful machine learning approach to automated detection, but a severe class imbalance in the dataset led to uns...

It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verbnoun pair taken from a predefined ontology describing typical “email speech acts”. We demonstrate that, although this categorization problem is quite dif...