In this work I address the challenge of augmenting n-gram language models according to prior linguistic intuitions. I argue that the family of hierarchical Pitman-Yor language models is an attractive vehicle through which to address the problem, and demonstrate the approach by proposing a model for German compounds. In an empirical evaluation, the model outperforms the Kneser-Ney model in terms of perplexity, and achieves preliminary improvements in English-German translation.
We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram language models as well as word dictionaries. Bayesian inference is performed on our model using an efﬁcient sampling technique. We evaluate the quality of the Bayesian decipherment output on simple and homophonic letter substitution ciphers and show that unlike a previous approach, our method consistently produces almost 100% accurate decipherments. ...
Null hypothesis signiﬁcance testing (NHST) is one of the main research tools in
social and behavioral research. It requires the speciﬁcation of a null hypothesis,
an alternative hypothesis, and data in order to test the null hypothesis. The
main result of a NHST is a p-value . An example of a null hypothesis and
a corresponding alternative hypothesis for a one-way analysis of variance is:
Medical Statistics at a Glance is directed at undergraduate
medical students, medical researchers, postgraduates in the
biomedical disciplines and at pharmaceutical industry personnel.
All of these individuals will, at some time in their
professional lives, be faced with quantitative results (their
own or those of others) that will need to be critically
evaluated and interpreted, and some, of course, will have to
pass that dreaded statistics exam! A proper understanding
of statistical concepts and methodology is invaluable for
Evaluating mutual fund performance is a topic of long-standing interest in the academic
literature, but few if any studies have addressed the selection of an optimal portfolio of funds.
Instead of using the historical data to estimate performance measures or produce fund rank-
ings, this study uses the data to explore the mutual-fund investment decision.
objective or subjective, when making decisions under uncertainty. This is especially true
when the consequences of the decisions can have a significant impact, financial or
otherwise. Most of us make everyday personal decisions this way, using an intuitive process
based on our experience and subjective judgments.
Mainstream statistical analysis, however, seeks objectivity by generally restricting the
information used in an analysis to that obtained from a current set of clearly relevant data.
We investigate the task of unsupervised constituency parsing from bilingual parallel corpora. Our goal is to use bilingual cues to learn improved parsing models for each language and to evaluate these models on held-out monolingual test data. We formulate a generative Bayesian model which seeks to explain the observed parallel data through a combination of bilingual and monolingual parameters. To this end, we adapt a formalism known as unordered tree alignment to our probabilistic setting.
Educators are interested in essay evaluation systems that include feedback about writing features that can facilitate the essay revision process. For instance, if the thesis statement of a student’s essay could be automatically identified, the student could then use this information to reflect on the thesis statement with regard to its quality, and its relationship to other discourse elements in the essay. Using a relatively small corpus of manually annotated data, we use Bayesian classification to identify thesis statements.
This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially inﬁnite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows signiﬁcant improvements of the models when compared against two baselines for this task.
This paper presents a set of Bayesian methods for automatically extending the W ORD N ET ontology with new concepts and annotating existing concepts with generic property ﬁelds, or attributes. We base our approach on Latent Dirichlet Allocation and evaluate along two dimensions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to W ORD N ET concepts. In all cases we ﬁnd that the principled LDA-based approaches outperform previously proposed heuristic methods, greatly improving the speciﬁcity of attributes at each concept. ...
In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classiﬁcations (Levin, 1993). We perform the classiﬁcation experiments using Bayesian Multinomial Regression (an efﬁcient log-linear modeling framework which we found to outperform SVMs for this task) with the proposed feature spaces. Our experiments suggest that subcategorization frames are not the most effective features for automatic verb classiﬁcation. A mixture of syntactic information and lexical information works best for this task. ...