Viterbi algorithm

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  • Sequential modeling has been widely used in a variety of important applications including named entity recognition and shallow parsing. However, as more and more real time large-scale tagging applications arise, decoding speed has become a bottleneck for existing sequential tagging algorithms. In this paper we propose 1-best A*, 1-best iterative A*, k-best A* and k-best iterative Viterbi A* algorithms for sequential decoding.

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  • Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Minimum decoding trellis length and truncation depth of wrap-around Viterbi algorithm for TBCC in mobile WiMAX

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  • Many different metrics exist for evaluating parsing results, including Viterbi, Crossing Brackets Rate, Zero Crossing Brackets Rate, and several others. However, most parsing algorithms, including the Viterbi algorithm, attempt to optimize the same metric, namely the probability of getting the correct labelled tree. By choosing a parsing algorithm appropriate for the evaluation metric, better performance can be achieved.

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  • Standard approaches to Chinese word segmentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the character marks a word boundary. Discriminatively trained models based on local character features are used to make the tagging decisions, with Viterbi decoding finding the highest scoring segmentation. In this paper we propose an alternative, word-based segmentor, which uses features based on complete words and word sequences.

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  • We present an iterative procedure to build a Chinese language model (LM). We segment Chinese text into words based on a word-based Chinese language model. However, the construction of a Chinese LM itself requires word boundaries. To get out of the chicken-and-egg problem, we propose an iterative procedure that alternates two operations: segmenting text into words and building an LM. Starting with an initial segmented corpus and an LM based upon it, we use a Viterbi-liek algorithm to segment another set of data. Then, we build an LM based on the second set and use the resulting LM to...

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  • Today, we are going to talk about: Another class of linear codes, known as Convolutional codes. Structures of the encoder and different ways for representing it.: state diagram and trellis representation of the code. What is a Maximum likelihood decoder? How the decoding is performed for Convolutional codes (the Viterbi algorithm) ?

    ppt38p stevenhuynh87 08-12-2010 106 36   Download

  • How the decoding is performed for Convolutional codes? -What is a Maximum likelihood decoder? -What are the soft decisions and hard decisions? -How does the Viterbi algorithm work?

    pdf29p huemanvdoc 23-11-2009 75 8   Download

  • Another class of linear codes, known as Convolutional codes. We studied the structure of the encoder and different ways for representing it.What are the state diagram and trellis representation of the code? How the decoding is performed for Convolutional codes? What is a Maximum likelihood decoder? What are the soft decisions and hard decisions? How does the Viterbi algorithm work?

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  • How decoding is performed for Convolutional codes? What is a Maximum likelihood decoder? What are soft decisions and hard decisions? How does the Viterbi algorithm work?The demodulator makes a firm or hard decision whether one or zero was transmitted and provides no other information for the decoder such as how reliable the decision is.

    ppt29p doanhung_dtvtk10 24-03-2013 32 4   Download

  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training...

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  • We would like to draw attention to Hidden Markov Tree Models (HMTM), which are to our knowledge still unexploited in the field of Computational Linguistics, in spite of highly successful Hidden Markov (Chain) Models. In dependency trees, the independence assumptions made by HMTM correspond to the intuition of linguistic dependency. Therefore we suggest to use HMTM and tree-modified Viterbi algorithm for tasks interpretable as labeling nodes of dependency trees.

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  • Often one may wish to learn a tree-to-tree mapping, training it on unaligned pairs of trees, or on a mixture of trees and strings. Unlike previous statistical formalisms (limited to isomorphic trees), synchronous TSG allows local distortion of the tree topology. We reformulate it to permit dependency trees, and sketch EM/Viterbi algorithms for alignment, training, and decoding.

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  • This paper presents a novel statistical model for automatic identification of English baseNP. It uses two steps: the Nbest Part-Of-Speech (POS) tagging and baseNP identification given the N-best POS-sequences. Unlike the other approaches where the two steps are separated, we integrate them into a unified statistical framework. Our model also integrates lexical information. Finally, Viterbi algorithm is applied to make global search in the entire sentence, allowing us to obtain linear complexity for the entire process. ...

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  • Given the new unusual and usual event models, both adapted from the general usual event model, the HMM topology is changed with one more state. Hence the cur- rent HMM has 2 states, one representing the usual events and one representing the first detected unusual event. The Viterbi algorithm is then used to find the best possible state sequence which could have emitted the observation sequence, according to the maximum likelihood (ML) cri- terion (Figure 2, step 3). Transition points, which define new segments, are detected using the current HMM topol- ogy and parameters.

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  • The Viterbi algorithm is the conventional decoding algorithm most widely adopted for sequence labeling. Viterbi decoding is, however, prohibitively slow when the label set is large, because its time complexity is quadratic in the number of labels. This paper proposes an exact decoding algorithm that overcomes this problem. A novel property of our algorithm is that it efficiently reduces the labels to be decoded, while still allowing us to check the optimality of the solution.

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  • Letter-substitution ciphers encode a document from a known or hypothesized language into an unknown writing system or an unknown encoding of a known writing system. It is a problem that can occur in a number of practical applications, such as in the problem of determining the encodings of electronic documents in which the language is known, but the encoding standard is not. It has also been used in relation to OCR applications. In this paper, we introduce an exact method for deciphering messages using a generalization of the Viterbi algorithm. ...

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  • There are two decoding algorithms essential to the area of natural language processing. One is the Viterbi algorithm for linear-chain models, such as HMMs or CRFs. The other is the CKY algorithm for probabilistic context free grammars. However, tasks such as noun phrase chunking and relation extraction seem to fall between the two, neither of them being the best fit. Ideally we would like to model entities and relations, with two layers of labels.

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  • The two fundamental building blocks of a digital communication system are modulation and channel coding. They enable reliable communication by providing signaling schemes and receiver structures that utilize the available spectrum and power efficiently.

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  • We propose a new specifically designed method for paraphrase generation based on Monte-Carlo sampling and show how this algorithm is suitable for its task. Moreover, the basic algorithm presented here leaves a lot of opportunities for future improvement. In particular, our algorithm does not constraint the scoring function in opposite to Viterbi based decoders. It is now possible to use some global features in paraphrase scoring functions. This algorithm opens new outlooks for paraphrase generation and other natural language processing applications like statistical machine translation.

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  • Hierarchical A∗ (HA∗ ) uses of a hierarchy of coarse grammars to speed up parsing without sacrificing optimality. HA∗ prioritizes search in refined grammars using Viterbi outside costs computed in coarser grammars. We present Bridge Hierarchical A∗ (BHA∗ ), a modified Hierarchial A∗ algorithm which computes a novel outside cost called a bridge outside cost.

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