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    IJCNLP
    2005
    Springer
    193views Natural Language Processing» more  IJCNLP 2005»
    A Chunking Strategy Towards Unknown Word Detection in Chinese Word Segmentation
    14 years 27 days ago
    A Chunking Strategy Towards Unknown Word Detection in Chinese Word Segmentation
    Download  nlp.suda.edu.cn
    This paper proposes a chunking strategy to detect unknown words in Chinese word segmentation. First, a raw sentence is pre-segmented into a sequence of word atoms 1 using a maximum...
    Guodong Zhou
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    IANDC
    2008
    114views more  IANDC 2008»
    Generalizing the Paige-Tarjan algorithm by abstract interpretation
    13 years 7 months ago
    Generalizing the Paige-Tarjan algorithm by abstract interpretation
    Download  www.math.unipd.it
    act Interpretation Francesco Ranzato and Francesco Tapparo Dipartimento di Matematica Pura ed Applicata, Universit`a di Padova, Italy The Paige and Tarjan algorithm (PT) for comp...
    Francesco Ranzato, Francesco Tapparo
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    FLAIRS
    2008
    188views Artificial Intelligence» more  FLAIRS 2008»
    Learning a Probabilistic Model of Event Sequences from Internet Weblog Stories
    13 years 9 months ago
    Learning a Probabilistic Model of Event Sequences from Internet Weblog Stories
    Download  www.reidswanson.com
    One of the central problems in building broad-coverage story understanding systems is generating expectations about event sequences, i.e. predicting what happens next given some a...
    Mehdi Manshadi, Reid Swanson, Andrew S. Gordon
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    JMLR
    2012
    203views Programming Languages» more  JMLR 2012»
    Maximum Margin Temporal Clustering
    11 years 9 months ago
    Maximum Margin Temporal Clustering
    Download  www.humansensing.cs.cmu.edu
    Temporal Clustering (TC) refers to the factorization of multiple time series into a set of non-overlapping segments that belong to k temporal clusters. Existing methods based on e...
    Minh Hoai Nguyen, Fernando De la Torre
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    CVPR
    2009
    IEEE
    1390views Computer Vision» more  CVPR 2009»
    Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
    15 years 2 months ago
    Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
    Download  www.cs.sfu.ca
    In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
    Mohammad Norouzi (Simon Fraser University), Mani R...
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