Recovering non-local dependencies for Chinese

To date, work on Non-Local Dependencies (NLDs) has focused almost exclusively on English and it is an open research question how well these approaches migrate to other languages. This paper surveys non-local dependency constructions in Chinese as represented in the Penn Chinese Treebank (CTB) and provides an approach for generating proper predicate-argument-modifier structures including NLDs from surface contextfree phrase structure trees. Our approach recovers non-local dependencies at the level of Lexical-Functional Grammar f-structures, using automatically acquired subcategorisation frames and f-structure paths linking antecedents and traces in NLDs. Currently our algorithm achieves 92.2% f-score for trace insertion and 84.3% for antecedent recovery evaluating on gold-standard CTB trees, and 64.7% and 54.7%, respectively, on CTBtrained state-of-the-art parser output trees

Treebank-based acquisition of LFG resources for Chinese

This paper presents a method to automatically acquire wide-coverage, robust, probabilistic Lexical-Functional Grammar resources for Chinese from the Penn Chinese Treebank (CTB). Our starting point is the earlier, proofof- concept work of (Burke et al., 2004) on automatic f-structure annotation, LFG grammar acquisition and parsing for Chinese using the CTB version 2 (CTB2). We substantially extend and improve on this earlier research as regards coverage, robustness, quality and fine-grainedness of the resulting LFG resources. We achieve this through (i) improved LFG analyses for a number of core Chinese phenomena; (ii) a new automatic f-structure annotation architecture which involves an intermediate dependency representation; (iii) scaling the approach from 4.1K trees in CTB2 to 18.8K trees in CTB version 5.1 (CTB5.1) and (iv) developing a novel treebank-based approach to recovering non-local dependencies (NLDs) for Chinese parser output. Against a new 200-sentence good standard of manually constructed f-structures, the method achieves 96.00% f-score for f-structures automatically generated for the original CTB trees and 80.01%for NLD-recovered f-structures generated for the trees output by Bikel’s parser

Accelerating MRI Data Acquisition Using Parallel Imaging and Compressed Sensing

Magnetic Resonance Imaging (MRI) scanners are one of important medical instruments, which can achieve more information of soft issues in human body than other medical instruments, such as Ultrasound, Computed Tomography (CT), Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), etc. But MRI\u27s scanning is slow for patience of doctors and patients. In this dissertation, the author proposes some methods of parallel imaging and compressed sensing to accelerate MRI data acquisition. Firstly, a method is proposed to improve the conventional GRAPPA using cross-sampled auto-calibration data. This method use cross-sampled auto-calibration data instead of the conventional parallel-sampled auto-calibration data to estimate the linear kernel model of the conventional GRAPPA. The simulations and experiments show that the cross-sampled GRAPPA can decrease the quantity of ACS lines and reduce the aliasing artifacts comparing to the conventional GRAPPA under same reduction factors. Secondly, a Hybrid encoding method is proposed to accelerate the MRI data acquisition using compressed sensing. This method completely changes the conventional Fourier encoding into Hybrid encoding, which combines the benefits of Fourier and Circulant random encoding, under 2D and 3D situation, through the proposed special hybrid encoding pulse sequences. The simulations and experiments illustrate that the images can be reconstructed by the proposed Hybrid encoding method to reserve more details and resolutions than the conventional Fourier encoding method. Thirdly, a pseudo 2D random sampling method is proposed by dynamically swapping the gradients of x and y axes on pulse sequences, which can be implemented physically as the convention 1D random sampling method. The simulations show that the proposed method can reserve more details than the convention 1D random sampling method. These methods can recover images to achieve better qualities under same situations than the conventional methods. Using these methods, the MRI data acquisitions can be accelerated comparing to the conventional methods