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    FLAIRS
    2007
    166views Artificial Intelligence» more  FLAIRS 2007»
    Managing Dynamic Contexts Using Failure-Driven Stochastic Models
    13 years 11 months ago
    Managing Dynamic Contexts Using Failure-Driven Stochastic Models
    Download  www.aaai.org
    We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three lay...
    Nikita A. Sakhanenko, George F. Luger, Carl R. Ste...
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    ICMLA
    2009
    169views Machine Learning» more  ICMLA 2009»
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    13 years 6 months ago
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures
    Download  www.ipb.uni-bonn.de
    Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
    Johannes Hartz
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    IJCAI
    2007
    180views Artificial Intelligence» more  IJCAI 2007»
    Dynamically Weighted Hidden Markov Model for Spam Deobfuscation
    13 years 10 months ago
    Dynamically Weighted Hidden Markov Model for Spam Deobfuscation
    Download  www.ijcai.org
    Spam deobfuscation is a processing to detect obfuscated words appeared in spam emails and to convert them back to the original words for correct recognition. Lexicon tree hidden M...
    Seunghak Lee, Iryoung Jeong, Seungjin Choi
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    NIPS
    2001
    178views Information Technology» more  NIPS 2001»
    Natural Language Grammar Induction Using a Constituent-Context Model
    13 years 10 months ago
    Natural Language Grammar Induction Using a Constituent-Context Model
    Download  www.cs.berkeley.edu
    This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according...
    Dan Klein, Christopher D. Manning
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    EMNLP
    2009
    99views Natural Language Processing» more  EMNLP 2009»
    A Unified Model of Phrasal and Sentential Evidence for Information Extraction
    13 years 6 months ago
    A Unified Model of Phrasal and Sentential Evidence for Information Extraction
    Download  www.cs.utah.edu
    Information Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, howev...
    Siddharth Patwardhan, Ellen Riloff
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