Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

We describe an open-source toolkit for statistical machine translation whose novel
contributions are (a) support for linguistically motivated factors,(b) confusion network …

This paper investigates the role of resource allocation as a source of processing difficulty in
human sentence comprehension. The paper proposes a simple information-theoretic …

This article describes three statistical models for natural language parsing. The models
extend methods from probabilistic context-free grammars to lexicalized grammars, leading to …

Each year the Conference on Computational Natural Language Learning (CoNLL) 1
features a shared task, in which participants train and test their systems on exactly the same …

This paper presents a deterministic parsing algorithm for projective dependency grammar.
The running time of the algorithm is linear in the length of the input string, and the …

We formalize weighted dependency parsing as searching for maximum spanning trees
(MSTs) in directed graphs. Using this representation, the parsing algorithm of Eisner (1996) …

Parsing unrestricted text is useful for many language technology applications but requires
parsing methods that are both robust and efficient. MaltParser is a language-independent …

We present an effective training algorithm for linearly-scored dependency parsers that
implements online largemargin multi-class training (Crammer and Singer, 2003; Crammer et …

We present a simple method for transferring dependency parsers from source languages
with labeled training data to target languages without labeled training data. We first …