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Language modeling for syntax-based machine translation using tree substitution grammars: A case study on Chinese-english translation

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ACM Transactions on Asian Language Information processing 2011年第4期10卷 1–29页

作者： Xiao, Tong Zhu, Jingbo Zhu, Muhua Northeastern University Key Laboratory of Medical Image Computing Natural Language Processing Lab Shenyang 110004 China

The poor grammatical output of Machine Translation (MT) systems appeals syntax-based approaches within language modeling. However, previous studies showed that syntax-based language modeling using (Context- Free) Treebank Grammars was not very helpful in improving BLEU scores for Chinese-English machine translation. In this article we further study this issue in the context of Chinese-English syntax-based Statistical Machine Translation (SMT) where Synchronous Tree Substitution Grammars (STSGs) are utilized to model the translation process. In particular, we develop a Tree Substitution Grammar-based language model for syntax-based MT, and present three methods to efficiently integrate the proposed language model into MT decoding. In addition, we design a simple and effective method to adapt syntax-based language models for MT tasks. We demonstrate that the proposed methods are able to benefit a state-of-the-art syntax-based MT system. On the NIST Chinese-English MT evaluation corpora, we finally achieve an improvement of 0.6 BLEU points over the baseline. © 2011 ACM.

关键词： Machine translation

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Automatic treebank conversion via informed decoding-A case study on Chinese treebanks

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ACM Transactions on Asian Language Information processing 2011年第3期10卷 1–24页

作者： Zhu, Muhua Zhu, Jingbo Xiao, Tong Key Laboratory of Medical Image Computing Natural Language Processing Lab Northeastern University Shenyang 110819 China

Treebanks are valuable resources for syntactic parsing. For some languages such as Chinese, we can obtain multiple constituency treebanks which are developed by different organizations. However, due to discrepancies of underlying annotation standards, such treebanks in general cannot be used together through direct data combination. To enlarge training data for syntactic parsing, we focus in this article on the challenge of unifying standards of disparate treebanks by automatically converting one treebank (source treebank) to fit a different standard which is exhibited by another treebank (target treebank). We propose to convert a treebank in two sequential steps which correspond to the part-of-speech level and syntactic structure level (including tree structures and grammar labels), respectively. Approaches used in both levels can be unified as an informed decoding procedure, where information derived from original annotation in a source treebank is used to guide the conversion conducted by a POS tagger (or a parser in the syntactic structure level) trained on a target treebank. We take two Chinese treebanks as a case study, and experiments on these two treebanks show significant improvements in conversion accuracy over baseline systems, especially in situations where a target treebank is small in size. © 2011 ACM.

关键词： Decoding

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NiuTrans: An open source toolkit for phrase-based and syntax-based machine translation 50

NiuTrans: An open source toolkit for phrase-based and syntax...

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Xiao, Tong Zhu, Jingbo Zhang, Hao Li, Qiang Natural Language Processing Lab Northeastern University Key Laboratory of Medical Image Computing Ministry of Education

ISBN: (纸本)9781937284275

We present a new open source toolkit for phrase-based and syntax-based machine translation. The toolkit supports several state-of-the-art models developed in statistical machine translation, including the phrase-based model, the hierachical phrase-based model, and various syntax-based models. The key innovation provided by the toolkit is that the decoder can work with various grammars and offers different choices of decoding algrithms, such as phrase-based decoding, decoding as parsing/tree-parsing and forest-based decoding. Moreover, several useful utilities were distributed with the toolkit, including a discriminative reordering model, a simple and fast language model, and an implementation of minimum error rate training for weight tuning. © 2012 Association for Computational Linguistics

关键词： Machine translation

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Automatic segmentation of Cerebral Computerized Tomography based on Parameter-Limited Gaussian Mixture Model

Automatic segmentation of Cerebral Computerized Tomography b...

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2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

作者： Jiang, Shaofeng Chen, Wufan Feng, Qianjin Chen, Zhen Key Lab for Medical Image Processing South Medical University Guang zhou China Nanchang Institute of Aeronautical Technology Nanchang China

ISBN: (纸本)1424411203

This paper realizes a new method to segment intracranial structure from series of Cerebral Computerized Tomography (CT) automatically. Firstly, a region growing and morphology based approach is developed to extract intracranial structures from series cerebral computerized tomography with the knowledge of anatomy, and then focusing on the problems of parameter initialization of the expectation maximization (EM) algorithm, an improved EM algorithm based on Parameter-Limited Gaussian Mixture Model is presented to segment intracranial structures successfully. Experiment shows that this method is successful on all cerebral computerized tomography from bottom to top part of cerebra. © 2007 IEEE.

关键词： image segmentation

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An empirical study of translation rule extraction with multiple parsers

An empirical study of translation rule extraction with multi...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Xiao, Tong Zhu, Jingbo Zhang, Hao Zhu, Muhua Natural Language Processing Lab. Northeastern University China Key Laboratory of Medical Image Computing Ministry of Education China

Translation rule extraction is an important issue in syntax-based Statistical Machine Translation (SMT). Recent studies show that rule coverage is one of the key factors affecting the success of syntaxbased systems. In this paper, we first present a simple and effective method to improve rule coverage by using multiple parsers in translation rule extraction, and then empirically investigate the effectiveness of our method on Chinese- English translation tasks. Experimental results show that extracting translation rules using multiple parsers improves a string-to-tree system by over 0.9 BLEU points on both NIST 2004 and 2005 test corpora.

关键词： Extraction

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MR image segmentation by nonparametric model

MR image segmentation by nonparametric model

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2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011

作者： Lu, Yi-Su Chen, Wu-Fan Key Lab for Medical Image Processing Southern Medical University Guangzhou China Electronic Engineering Department South China Institute of Software Engineering Guangzhou China

ISBN: (纸本)9781424493524

Nonparametric Dirichlet Process Mixtures (MDP) model algorithm is applied to segment images, which can obtain the segmentation class numbers automatically without initialization. The algorithm is used to segment noisy natural images and magnetic resonance images with biasing field. Compared with classical Markov Field (MRF) segmentation, the nonparametric segmentation results show the greater performance. This method is also analyzed quantitatively on the belly magnetic resonance images. The Dice Similarity Coefficients (DSC) of all slices exceed 93%, which show that the proposed method is robust and accurate. © 2011 IEEE.

关键词： Magnetic resonance

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A new medical image segmentation algorithm

A new medical image segmentation algorithm

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2011 International Conference on Computational and Information Sciences, ICCIS 2011

作者： Lu, Yi-Su Chen, Wu-Fan Key Lab. for Medical Image Processing Southern Medical University Guangzhou China Electronic Engineering Department South China Institute of Software Engineering Guangzhou China

ISBN: (纸本)9780769545011

Nonparametric Dirichlet Process Mixtures (MDP) model algorithm is applied to segment images, which can obtain the segmentation class numbers automatically without initialization. In this paper a modified Dirichlet process mixtures model constrained by Markov random field (MRF) prior is constructed, which can smooth MDP image segmentation result and control segmentation classes effectively. Many comparative experiments such as noisy natural images and magnetic resonance images are segmented by classical MDP model and the modified algorithm. The results show the proposed method is robust and accurate. © 2011 IEEE.

关键词： image segmentation

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Hotspots on modern medical imaging and image analysis

Hotspots on modern medical imaging and image analysis

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IEEE/EMBS International Summer School on medical Devices and Biosensors

作者： Wu-fan Chen Key Lab for Medical Image Processing of Guangdong Province China

Vision information processing (VIP) is the important supports of image processing. Currently, VIP has become as a leader direction in remote sense image processing (which mostly serves the government and military) and in medical image processing (which widely serves a great numbers of patients). medical imaging and medical image analysis techniques are the hotspots of the biomedical engineering. On one hand, different from optical imaging, modern medical imaging such as CT, MR and PET have the very complex imaging mechanisms with deep mathematic fundamental and are entirely different with each other. Advanced and novel precise techniques in medical imagine are also needed, such as design of various filters, calibration techniques for different artifacts, fast imaging methods and low dose CT reconstruction. New important applications of medical imaging are still in progress with high speed, which include special imaging technique to some diseases, multi-functional imaging equipments and special imaging devices for brain or hand. On the other hand, medical image analysis such as image segmentation and image registration as the basic medical image processing still require some novel techniques to make it more effectively. Fuzziness of physiology estimation, stochastic behavior of imaging process, and ill condition behavior of mathematic model may be the dispute problems for a long time. For more convenient, we are to quite our recent research results to illustrate above the hotspots.

关键词： Biomedical imaging image analysis Optical imaging image processing Biomedical image processing Computed tomography Mathematics Information processing Remote sensing Government

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Multi-class segmentation based on generalized fuzzy Gibbs random fields

Multi-class segmentation based on generalized fuzzy Gibbs ra...

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Proceedings: 2003 International Conference on image processing, ICIP-2003

作者： Lin, Yazhong Chen, Wufan Chan, Francis H.Y. Key Lab. for Med. Image Processing First Military Medical University Guangzhou China Department of Electrical Engineering University of Hong Kong Hong Kong Hong Kong

The model of Gibbs random fields is widely applied to Bayesian segmentation due to its best property of describing the spatial constraint information. However, the general segmentation methods, whose model is defined only on hard levels but not on fuzzy set, may come across a lot of difficulties, e.g., getting the unexpected results or even nothing, especially when the blurred or degraded images are considered In this paper, two multi-class approaches, based on the model of Piecewise Fuzzy Gibbs Random Fields (PFGRF) and that of Generalized Fuzzy Gibbs Random Fields (GFGRF) respectively, are presented to address these difficulties. In our experiments, both magnetic resonance image and simulated image are implemented with the two approaches mentioned above and the classical "hard" one. These three different results show that the approach of GFGRF is an efficient and unsupervised technique, which can automatically and optimally segment the images to be finer.

关键词： image segmentation

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Rapid and multimodal in vivo bioimaging of cancer cells through in situ biosynthesis of Zn＆Fe nanoclusters

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Nano Research 2017年第8期10卷 2626-2632页

作者： Tianyu Du Chunqiu Zhao Fawad ur Rehman Lanmei Lai Xiaoqi Li Yi Sun Shouhua Luo Hui Jiang Matthias Selke Xuemei Wang State Key Laboratory of Bioelectronics (Chien-Shiung Wu Lab) School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Nanjing Foreign Language School Nanjing 210096 China Laboratory of the signal and image processing School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China Department of Chemistry and Biochemistry California State University Los Angeles CA 90032 USA

Early diagnosis remains highly important for efficient cancer treatment, and hence, there is significant interest in the development of effective imaging strategies. This work reports a new multimodal bioimaging method for accurate and rapid diagnosis of cancer cells by introducing aqueous Fe^2＋ and Zn^2＋ ions into cancer cells （i.e., HeLa, U87, and HepG2 cancer cells）. We found that the biocompatible metal ions Fe^2＋ and Zn^2＋ forced the cancer cells to spontaneously synthesize fluorescent ZnO nanoclusters and magnetic Fe3O4 nanoclusters. These clusters could then be used for multimodal cancer imaging by combining fluorescence imaging with magnetic resonance imaging and computed tomography imaging. Meanwhile, for normal cells （i.e., L02） and tissues, neither fluorescence nor any other obvious difference could be detected between pre- and post-injection. This multimodal bioimaging strategy based on the in situ biosynthesized Zn＆Fe oxide nanoclusters might therefore be useful for early cancer diagnosis and therapy.

关键词： multimodal imaging biosynthesized Zn＆Fe oxide nanoclusters cancer diagnosis

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