1,208 research outputs found
A Web-Based Tool for Analysing Normative Documents in English
Our goal is to use formal methods to analyse normative documents written in
English, such as privacy policies and service-level agreements. This requires
the combination of a number of different elements, including information
extraction from natural language, formal languages for model representation,
and an interface for property specification and verification. We have worked on
a collection of components for this task: a natural language extraction tool, a
suitable formalism for representing such documents, an interface for building
models in this formalism, and methods for answering queries asked of a given
model. In this work, each of these concerns is brought together in a web-based
tool, providing a single interface for analysing normative texts in English.
Through the use of a running example, we describe each component and
demonstrate the workflow established by our tool
Structural Equity: Big-Picture Thinking & Partnerships That Improve Community College Student Outcomes
While access to higher education has grown considerably for low-income students and students of color over the past decades, the rates at which those students succeed in completing or transferring to a four-year university remain low and have been slow to improve. This report describes how four successful community colleges have cultivated robust, cross-sector partnerships to create seamless educational pathways for students, and highlights three specific strategies the institutions have used to help eliminate structural barriers that perpetuate student success gaps along racial/ethnic and socioeconomic lines. Development of this guide was supported by the Lumina Foundation
Recognizing cited facts and principles in legal judgements
In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, Îș=0.65 and Îș=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall Îș of 0.72 with the human-annotated gold standard
A Hierarchical Bayesian Model of Pitch Framing
Since the advent of high-resolution pitch tracking data (PITCHf/x), many in
the sabermetrics community have attempted to quantify a Major League Baseball
catcher's ability to "frame" a pitch (i.e. increase the chance that a pitch is
called as a strike). Especially in the last three years, there has been an
explosion of interest in the "art of pitch framing" in the popular press as
well as signs that teams are considering framing when making roster decisions.
We introduce a Bayesian hierarchical model to estimate each umpire's
probability of calling a strike, adjusting for pitch participants, pitch
location, and contextual information like the count. Using our model, we can
estimate each catcher's effect on an umpire's chance of calling a strike.We are
then able to translate these estimated effects into average runs saved across a
season. We also introduce a new metric, analogous to Jensen, Shirley, and
Wyner's Spatially Aggregate Fielding Evaluation metric, which provides a more
honest assessment of the impact of framing
A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?
Predicting historic temperatures based on tree rings, ice cores, and other
natural proxies is a difficult endeavor. The relationship between proxies and
temperature is weak and the number of proxies is far larger than the number of
target data points. Furthermore, the data contain complex spatial and temporal
dependence structures which are not easily captured with simple models. In this
paper, we assess the reliability of such reconstructions and their statistical
significance against various null models. We find that the proxies do not
predict temperature significantly better than random series generated
independently of temperature. Furthermore, various model specifications that
perform similarly at predicting temperature produce extremely different
historical backcasts. Finally, the proxies seem unable to forecast the high
levels of and sharp run-up in temperature in the 1990s either in-sample or from
contiguous holdout blocks, thus casting doubt on their ability to predict such
phenomena if in fact they occurred several hundred years ago. We propose our
own reconstruction of Northern Hemisphere average annual land temperature over
the last millennium, assess its reliability, and compare it to those from the
climate science literature. Our model provides a similar reconstruction but has
much wider standard errors, reflecting the weak signal and large uncertainty
encountered in this setting.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS398 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
The authors are doing the readers of Statistical Science a true service with
a well-written and up-to-date overview of boosting that originated with the
seminal algorithms of Freund and Schapire. Equally, we are grateful for
high-level software that will permit a larger readership to experiment with, or
simply apply, boosting-inspired model fitting. The authors show us a world of
methodology that illustrates how a fundamental innovation can penetrate every
nook and cranny of statistical thinking and practice. They introduce the reader
to one particular interpretation of boosting and then give a display of its
potential with extensions from classification (where it all started) to least
squares, exponential family models, survival analysis, to base-learners other
than trees such as smoothing splines, to degrees of freedom and regularization,
and to fascinating recent work in model selection. The uninitiated reader will
find that the authors did a nice job of presenting a certain coherent and
useful interpretation of boosting. The other reader, though, who has watched
the business of boosting for a while, may have quibbles with the authors over
details of the historic record and, more importantly, over their optimism about
the current state of theoretical knowledge. In fact, as much as ``the
statistical view'' has proven fruitful, it has also resulted in some ideas
about why boosting works that may be misconceived, and in some recommendations
that may be misguided. [arXiv:0804.2752]Comment: Published in at http://dx.doi.org/10.1214/07-STS242B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
The Transfer Playbook: Essential Practices For Two- And Four-year Colleges
Recognizing the critical need to help millions of community college students failed by current transfer practices and policies. A new report provides a detailed guide for two- and four-year colleges on how to improve bachelor's degree outcomes for students who start at community college.Every year, millions of students aiming to attain a bachelor's degree attend community colleges because of their affordability and accessibility. Most will not realize their goals. While the vast majority of students report they want to earn a bachelor's degree, only 14 percent of degree-seeking students achieve that goal within six years, according to recent research from CCRC, Aspen, and the National Student Clearinghouse Research Center. The odds are worse for low-income students, first-generation college students, and students of colorâthose most likely to start at a community college
Hierarchical Bayesian Modeling of Hitting Performance in Baseball
We have developed a sophisticated statistical model for predicting the
hitting performance of Major League baseball players. The Bayesian paradigm
provides a principled method for balancing past performance with crucial
covariates, such as player age and position. We share information across time
and across players by using mixture distributions to control shrinkage for
improved accuracy. We compare the performance of our model to current
sabermetric methods on a held-out season (2006), and discuss both successes and
limitations
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