This paper presents the prevalence rates of schizophrenia and major affective disorders by age and race among a random sample of male jail detainees. Subjects were administered the National Institute of Mental Health Diagnostic Interview Schedule (NIMH-DIS). The jail prevalence rates were then compared with general population data from the five-city Epidemiologic Catchment Area program using di...

The Microsoft Research translation system is a syntactically informed phrasal SMT system that uses a phrase translation model based on dependency treelets and a global reordering model based on the source dependency tree. These models are combined with several other knowledge sources in a log-linear manner. The weights of the individual components in the loglinear model are set by an automatic ...

We propose a new approach for identifying the support points of a locally optimal design when the model is a nonlinear model. In contrast to the commonly used geometric approach, we use an approach based on algebraic tools. Considerations are restricted to models with two parameters, and the general results are applied to often used special cases, including logistic, probit, double exponential ...

Abstract. The F&B-index is used to speed up pattern matching in tree and graph data, and is based on the maximum F&B-bisimulation, which can be computed in loglinear time for graphs. It has been shown that the maximum F-bisimulation can be computed in linear time for DAGs. We build on this result, and introduce a linear algorithm for computing the maximum F&B-bisimulation for tree data. It firs...

Low-resource named entity recognition is still an open problem in NLP. Most stateof-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world’s languages it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities...

Generalized linear models with group effects are commonly used in scientific studies. However, there appear to be no results for selecting optimal designs. In this paper, we identify the structure of locally optimal designs, provide a general strategy to determine the design points and the corresponding weights for optimal designs, and present theoretical results for the special case of D-optim...

In this paper we present several extensions of MARIE1, a freely available N -gram-based statistical machine translation (SMT) decoder. The extensions mainly consist of the ability to accept and generate word graphs and the introduction of two new N -gram models in the loglinear combination of feature functions the decoder implements. Additionally, the decoder is enhanced with a caching strategy...

This course introduces principles and analyses related to data with categorical outcomes. This course will consider topics such as probability distributions with categorical data, contingency table analysis, the generalized linear model, logit models and loglinear models. Students are expected to: a) learn to select methods appropriate for a question of interest for data with a categorical outc...

We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; an...

We present LEMMING, a modular loglinear model that jointly models lemmatization and tagging and supports the integration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or analyzers. LEMMING sets the new state of the art in token-based statistical lemmatization on six languages; e.g., for Czec...