eric ringger eegsiewmicrosoftcom call papers feature engineering machine learning natural language processing workshop annual meeting association computational linguistics acl 2005 httpresearchmicrosoftcomringgerfeatureengineeringworkshop submission deadline april 20 2005 ann arbor michigan june 29 2005 experience machine learning solving natural language processing tasks accumulates field practitioners finding feature engineering critical choice machine learning algorithm feature design feature selection feature impact ablation studies like significantly affect performance systems deserve greater attention wake shift away knowledge engineering successes datadriven statistical methods researchers field likely make progress incorporating additional sometimes familiar sources knowledge features although experience area feature engineering found theoretical machine learning community particular demands natural language processing leave much discovered workshop aims bring together practitioners nlp machine learning information extraction speech processing related fields intention sharing experimental evidence successful approaches feature engineering including feature design feature selection welcome papers address goals also seek distill best practices discover new sources knowledge features previously untapped workshop include invited talk andrew mccallum university massachusetts amherst submission submitted papers prepared pdf format fonts included microsoft word doc format longer 8 pages following acl style detailed information format submissions found httpwwwaclweborgacl2005indexphpstylefiles language workshop english submissions sent attachment following email address ringger microsoft dot com accepted papers presented oral sessions workshop collected printed proceedings submissions invited aspects feature engineering machine learning nlp topics may include necessarily limited novel methods discovering inducing features mining web closed classes useful indicator features comparative studies different feature selection algorithms nlp tasks interactive tools help researchers identify ambiguous cases could disambiguated addition features error analysis various aspects feature induction selection representation issues representation eg strategies handling hierarchical representations including decomposing atomic features employing statistical relational learning techniques used fields outside nlp prove useful nlp impact feature selection feature design practical considerations training time experimental design domain independence evaluation analysis feature engineering interaction specific machine learning methods commonly used nlp combining classifiers employ diverse types features studies methods defining feature set example iteratively expanding base feature set issues representing combining realvalued categorical features nlp tasks important dates paper submission deadline april 20 2005 noon pst gmt8 notification acceptance may 10 2005 submission cameraready copy may 17 2005 workshop june 29 2005 organization chair contact person eric ringger microsoft research one microsoft way redmond wa 98052 usa ringger microsoft dot com program committee simon corstonoliver microsoft research usa kevin duh university washington usa matthew richardson microsoft research usa oren etzioni university washington usa andrew mccallum university massachusetts amherst usa dan bikel ibm research usa olac fuentes inaoe mexico chris manning stanford university usa kristina toutanova stanford university usa hideki isozaki ntt communication science laboratories japan caroline sporleder university edinburgh uk tue 05 aug 2008 041721 0800 uai mlnet cfp acl 2005 workshop feature engineering machine learning nlp