A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Abstract Conditional Random Fields (CRFs) are a widely-used approach for supervised
sequence labelling, notably due to their ability to handle large description spaces and to …

This article considers approaches which rerank the output of an existing probabilistic parser.
The base parser produces a set of candidate parses for each input sentence, with …

We investigate some pitfalls regarding the discriminatory power of MT evaluation metrics
and the accuracy of statistical significance tests. In a discriminative reranking experiment for …

Markov networks are commonly used in a wide variety of applications, ranging from
computer vision, to natural language, to computational biology. In most current applications …

Probabilistic modeling of lexicalized grammars is difficult because these grammars exploit
complicated data structures, such as typed feature structures. This prevents us from applying …

We present a method to discover robust and interpretable sociolinguistic associations from
raw geotagged text data. Using aggregate demographic statistics about the authors' …

Sparsity-inducing regularization problems are ubiquitous in machine learning applications,
ranging from feature selection to model compression. In this paper, we present a novel …

Techniques are provided to determine user-interest features and user-interest parameter
weights for a user-interest model. The user-interest features are pre-determined and/or …

We present a hybrid MT architecture, combining state-of-the-art linguistic processing with
advanced stochastic techniques. Grounded in a theoretical reflection on the division of labor …