Learn More
We present a simple and effective semi-supervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and(More)
We present algorithms for higher-order dependency parsing that are " third-order " in the sense that they can evaluate sub-structures containing three dependencies, and " efficient " in the sense that they require only O(n 4) time. Importantly, our new parsers can utilize both sibling-style and grandchild-style interactions. We evaluate our parsers on the(More)
Syntactic parsing is a fundamental problem in computational linguistics and natural language processing. Traditional approaches to parsing are highly complex and problem specific. Recently, Sutskever et al. (2014) presented a task-agnostic method for learning to map input sequences to output sequences that achieved strong results on a large scale machine(More)
This paper introduces algorithms for non-projective parsing based on dual decomposition. We focus on parsing algorithms for non-projective head automata, a generalization of head-automata models to non-projective structures. The dual decomposition algorithms are simple and efficient, relying on standard dynamic programming and minimum spanning tree(More)
We describe a parsing approach that makes use of the perceptron algorithm, in conjunction with dynamic programming methods, to recover full constituent-based parse trees. The formalism allows a rich set of parse-tree features, including PCFG-based features, bigram and trigram dependency features , and surface features. A severe challenge in applying such an(More)
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG)(More)
This paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how partition functions and marginals for directed spanning trees can be computed by an adaptation of Kirchhoff's Matrix-Tree Theorem. To demonstrate an application of the method, we(More)
OBJECTIVE This study aimed at investigating the effects of sitting posture on ischial pressure and pelvic orientation for two types of cushions. DESIGN Two types of seating devices, Roho and Polyurethane (PU) Foam cushions, six predefined postures, and two subject groups, Normal and Paraplegic, were tested. Ischial pressure and pelvic orientation were(More)
OBJECTIVE This study aimed at estimating the musculotendon parameters of the prime elbow flexors in vivo for both normal and hemiparetic subjects. DESIGN A neuromusculoskeletal model of the elbow joint was developed incorporating detailed musculotendon modeling and geometrical modeling. BACKGROUND Neuromusculoskeletal modeling is a valuable tool in(More)
We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call " ParseySaurus, " uses the DRAGNN framework [Kong et al., 2017] to combine transition-based recurrent parsing and tagging with character-based word representations. On the v1.3 Universal Dependencies Treebanks, the new system outpeforms the publicly(More)