Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. Th...

We present a method to jointly learn features and weights directly from distributional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonological markedness constraints directly from the distribution of input-output mappings in an Optimality Theory (OT) setting. The model uses an Indian Buffet Process prior to learn the feature values used...

The index of dissimilarity, often denoted by Delta, is commonly used, especially in social science and with large datasets, to describe the lack of fit of models for categorical data. In this paper the definition and sampling properties of the index are investigated for general loglinear and log-multiplicative models. It is argued that in some applications a standardized version of the index is...

Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the model in the form of feature functions. Traditional differential evolution algorithm is easy to f...

1. Introduction. Illustrated manuscripts may be analysed in terms of both their texts and their illustrations. While statistical techniques have long been applied to the textual analysis of manuscripts, e.g., Lebart and Salem (1994), no corresponding pictorial analysis can be found in the art history literature. The purpose of this paper is to suggest that the field can benefit from statistical...

Conditional independence models are defined by a set of conditional independence restrictions and play an important role in many statistical applications, especially, but not only, graphical modeling. In this paper we identify a subclass of these models which are hierarchical marginal log-linear, as defined by Bergsma and Rudas (2002a). Such models are smooth, which implies the applicability of...

W consider a newsvendor who dynamically updates her forecast of the market demand over a finite planning horizon. The forecast evolves according to the martingale model of forecast evolution (MMFE). The newsvendor can place multiple orders with increasing ordering cost over time to satisfy demand that realizes at the end of the planning horizon. In this context, we explore the trade-off between...

Researchers have the need for improved coordination and continuity of care in health-care environments, but little research has been undertaken to better understand how coordination occurs and how it might be improved. Using Klein’s (2001) phases of coordination this exploratory study provides a profile of the contributions of role-based communications to team coordination in an Intensive Care ...

Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework is attractive because it often requires much less training time in practice than batch training algorithms. However, L1-regularization, which is becoming popular in natural language processing because of its ability to ...