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Semi-supervised domain adaptation for WSD: Using a word-by-word model selection approach

Semi-supervised domain adaptation for WSD: Using a word-by-w...

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IEEE International Conference on Cognitive Informatics

作者： Yuhang Guo Wanxiang Che Ting Liu Sheng Li Research Center of Information Retrieval MOE-Microsoft Key Laboratory of Natural Language Processing and Speech School of Computer Science and Technology Harbin Institute of Technology China

This paper proposes a word-by-word model selection approach to domain adaptation for Word Sense Disambiguation. By this approach, the model for a target word is automatically selected from a candidate model set, which is comprised of improved self-training models and a supervised model. The improved self-training uses sense priors to prevent its iteration from converging into undesirable states. Experimental results on a domain-specific corpus show that: (1) our improved self-training model is effective for the words which have target domain linked senses; (2) the selected models obtain higher accuracies than each single model and effectively improve the performance compared to the state-of-the-art supervised model.

关键词： Support vector machines Adaptation model Training Accuracy Finance Data models Training data

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Point process models of spectro-temporal modulation events for speech recognition

Point process models of spectro-temporal modulation events f...

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Asilomar Conference on Signals, Systems & computers

作者： Aren Jansen Nima Mesgarani Partha Niyogi Johns Hopkins University Baltimore MD US Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Neurological Surgery University of California San Francisco CA USA Departments of Computer Science and Statistics University of Chicago Chicago IL USA

Neurobiological research has uncovered the existence of cortical neurons in various animal species tuned to particular spectro-temporal modulations (STM) in the auditory stimulus. Other findings indicate that temporal statistics of the resulting neural spike trains may encode the underlying content of species-specific communication calls. With this motivation, we present an alternative approach to speech recognition based on point process statistical models of the local maxima events produced by a cortically-inspired spectro-temporal filter bank. We demonstrate the computational adequacy of this approach on the practical task of keyword spotting.

关键词： Hidden Markov models speech Acoustics Modulation speech recognition Training Detectors

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First- and second-order expectation semirings with applications to minimum-risk training on translation forests

First- and second-order expectation semirings with applicati...

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2009 Conference on Empirical Methods in Natural language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Li, Zhifei Eisner, Jason Department of Computer Science Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States

Many statistical translation models can be regarded as weighted logical deduction. Under this paradigm, we use weights from the expectation semiring (Eisner, 2002), to compute first-order statistics (e.g., the expected hypothesis length or feature counts) over packed forests of translations (lattices or hypergraphs). We then introduce a novel second-order expectation semiring, which computes second-order statistics (e.g., the variance of the hypothesis length or the gradient of entropy). This second-order semiring is essential for many interesting training paradigms such as minimum risk, deterministic annealing, active learning, and semi-supervised learning, where gradient descent optimization requires computing the gradient of entropy or risk. We use these semirings in an open-source machine translation toolkit, Joshua, enabling minimum-risk training for a benefit of up to 1.0 BLEU point. © 2009 ACL and AFNLP.

关键词： Entropy

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Efficient extraction of oracle-best translations from hypergraphs

Efficient extraction of oracle-best translations from hyperg...

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2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human language Technologies, NAACL-HLT 2009

作者： Li, Zhifei Khudanpur, Sanjeev Center for Language and Speech Processing Department of Computer Science The Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781932432428

Hypergraphs are used in several syntax-inspired methods of machine translation to compactly encode exponentially many translation hypotheses. The hypotheses closest to given reference translations therefore cannot be found via brute force, particularly for popular measures of closeness such as BLEU. We develop a dynamic program for extracting the so called oracle-best hypothesis from a hypergraph by viewing it as the problem of finding the most likely hypothesis under an n-gram language model trained from only the reference translations. We further identify and remove massive redundancies in the dynamic program state due to the sparsity of n-grams present in the reference translations, resulting in a very efficient program. We present run-time statistics for this program, and demonstrate successful application of the hypotheses thus found as the targets for discriminative training of translation system components. © 2009 Association for Computational Linguistics

关键词： Application programs

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Variational Decoding for Statistical Machine Translation

Variational Decoding for Statistical Machine Translation

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Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural language processing of the AFNLP, ACL-IJCNLP 2009

作者： Li, Zhifei Eisner, Jason Khudanpur, Sanjeev Department of Computer Science Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781617382581

Statistical models in machine translation exhibit spurious ambiguity. That is, the probability of an output string is split among many distinct derivations (e.g., trees or segmentations). In principle, the goodness of a string is measured by the total probability of its many derivations. However, finding the best string (e.g., during decoding) is then computationally intractable. Therefore, most systems use a simple Viterbi approximation that measures the goodness of a string using only its most probable derivation. Instead, we develop a variational approximation, which considers all the derivations but still allows tractable decoding. Our particular variational distributions are parameterized as n-gram models. We also analytically show that interpolating these n-gram models for different n is similar to minimum-risk decoding for BLEU (Tromble et al., 2008). Experiments show that our approach improves the state of the art. © 2009 ACL and AFNLP.

关键词： Decoding

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Speaker segmentation based on between-window correlation over speakers' characteristics

Speaker segmentation based on between-window correlation ove...

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Asia-Pacific Signal and Information processing Association 2009 Annual Summit and Conference, APSIPA ASC 2009

作者： Wang, Gang Zheng, Thomas Fang Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University Beijing 100084 China

Speaker segmentation is widely applied in many domains such as multi-peaker detection and speaker tracking. However, the performance of the conventional metric-based methods is neither good enough nor stable due to the stability of the between-window distance calculation. In order to enhance the stability and hence to improve the performance, a new method based on the between-window correlation over speakers' characteristics is proposed. In this method, a set of reference speaker models are trained which can represent the whole speaker model space. The between-window correlation of likelihood vectors of scores against these reference models is taken as the metric. The gender information and the Peak and Valley information are also used. Experiments over NIST SRE 2002 Segmentation BNEWS and SWBD Datasets show that better performance can be achieved compared with the BIC and the GLR methods. What's more, the proposed method can achieve approximately the best performance in a wider value range of predefined thresholds than the BIC and the GLR methods, which reduces the threshold sensitivity.

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An open source Tesseract based optical character recognizer for Bangla script

An open source Tesseract based optical character recognizer ...

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ICDAR2009 - 10th International Conference on Document Analysis and Recognition

作者： Hasnat, Md. Abul Chowdhury, Muttakinur Rahman Khan, Mumit Center for Research on Bangla Language Processing Department of Computer Science and Engineering BRAC University 66 Mohakhali Dhaka Bangladesh

ISBN: (纸本)9780769537252

BanglaOCR is currently the only open source optical character recognition (OCR) software for the Bangla (Bengali) script developed by the center for Research on Bangla language processing (CRBLP). Tesseract, maintained by Google, is considered to be one of the most accurate free open source OCR engines currently available. In this paper, we present a new OCR for the Bangla/Bengali script that combines the recognition power of Tesseract and the Bangla script processing power of BanglaOCR by integrating the Tesseract recognition engine into BanglaOCR. We first present the complete methodology to build the combined OCR, followed by the implementation strategy. In this paper, we focus on the training data preparation process, Tesseract integration procedure and the post-processing techniques. The techniques described in this paper can be readily applied to build OCRs for other scripts as well. © 2009 IEEE.

关键词： Engines

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On projections of Gaussian distributions using maximum likelihood criteria

On projections of Gaussian distributions using maximum likel...

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Information Theory and Applications Workshop, ITA

作者： Haolang Zhou Damianos Karakos Sanjeev Khudanpur Andreas G. Andreou Carey E. Priebe Department of Electrical and Computer Engineering and Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Center for Language and Speech Processing Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781424439904

Generative statistical models with a very large number of parameters are frequently used in real-world data applications, such as large-vocabulary speech recognition (LVCSR). Complex models are needed in order to capture the ubiquitous variability in the observed signal, but data sparsity causes significant problems in their training. One way of dealing with data sparsity is to perform dimensionality reduction of the observed features, with the goal of reducing the model parameter space without sacrificing performance. When the data are Gaussian distributed, the dimensionality reduction can be done efficiently using the maximum likelihood criterion; this leads to the heteroscedastic linear discriminant analysis (HLDA), which is a natural extension of linear discriminant analysis (LDA) to the case where the class-conditional Gaussians have unequal covariance matrices. A further extension of HLDA to multiple transforms (MLDA) can also be tackled efficiently. This paper presents the theory behind HLDA and MLDA, and demonstrates their performance with synthetic data.

关键词： Gaussian distribution Linear discriminant analysis Covariance matrix speech recognition Maximum likelihood estimation Mathematics Acoustics Training data Mel frequency cepstral coefficient Distributed computing

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An Open Source Tesseract Based Optical Character Recognizer for Bangla Script

An Open Source Tesseract Based Optical Character Recognizer ...

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International Conference on Document Analysis and Recognition

作者： Md. Abul Hasnat Muttakinur Rahman Chowdhury Mumit Khan Center of Research on Bangla Language Processing Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

关键词： Character recognition Optical character recognition software Search engines Open source software Training data Optical sensors Discrete cosine transforms Testing Graphical user interfaces Packaging

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Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus

Generating high-coverage semantic orientation lexicons from ...

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2009 Conference on Empirical Methods in Natural language processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Mohammad, Saif Dunne, Cody Dorr, Bonnie Laboratory for Computational Linguistics and Information Processing University of Maryland United States Human-Computer Interaction Lab. University of Maryland United States Institute for Advanced Computer Studies University of Maryland United States Department of Computer Science University of Maryland United States Human Language Technology Center of Excellence United States

Sentiment analysis often relies on a semantic orientation lexicon of positive and negative words. A number of approaches have been proposed for creating such lexicons, but they tend to be computationally expensive, and usually rely on significant manual annotation and large corpora. Most of these methods use WordNet. In contrast, we propose a simple approach to generate a high-coverage semantic orientation lexicon, which includes both individual words and multi-word expressions, using only a Roget-like thesaurus and a handful of affixes. Further, the lexicon has properties that support the Polyanna Hypothesis. Using the General Inquirer as gold standard, we show that our lexicon has 14 percentage points more correct entries than the leading WordNet-based high-coverage lexicon (SentiWordNet). In an extrinsic evaluation, we obtain significantly higher performance in determining phrase polarity using our thesaurus-based lexicon than with any other. Additionally, we explore the use of visualization techniques to gain insight into the our algorithm beyond the evaluations mentioned above. © 2009 ACL and AFNLP.

关键词： Thesauri

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