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We present a simple and effective semisupervised 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 Prague(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 nonprojective parsing based on dual decomposition. We focus on parsing algorithms for nonprojective 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 present algorithms for higher-order dependency parsing that are “third-order” in the sense that they can evaluate substructures containing three dependencies, and “efficient” in the sense that they require only O(n4) time. Importantly, our new parsers can utilize both sibling-style and grandchild-style interactions. We evaluate our parsers on the Penn(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 PCFGbased 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)
Neuromusculoskeletal (NMS) modeling is a valuable tool in orthopaedic biomechanics and motor control research. To evaluate the feasibility of using electromyographic (EMG) signals with NMS modeling to estimate individual muscle force during dynamic movement, an EMG driven NMS model of the elbow was developed. The model incorporates dynamical equation of(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)
OBJECTIVE To determine the distribution of weakness across elbow range of motion (ROM) in subjects with hemiparesis. DESIGN A detailed analysis of elbow torque and associated electromyographic signals of 5 prime elbow muscles generated during maximum isometric voluntary flexion (MIVF) and extension (MIVE) at 8 different elbow positions. SETTING(More)