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model_corpus
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<lexUnit xmlns="http://framenet.icsi.berkeley.edu" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" frame="Model" frameID="652" status="Finished_Initial" POS="N" name="model.n" ID="8538" totalAnnotated="11" xsi:schemaLocation="../schema/lexUnit.xsd">
    <header>
        <corpus description="BNC2" name="BNC2" ID="111">
            <document description="bncp" name="bncp" ID="421"/>
        </corpus>
        <frame>
            <FE fgColor="FFFFFF" bgColor="FF69B4" type="Core" abbrev="Model" name="Model"/>
            <FE fgColor="FFFFFF" bgColor="004C99" type="Peripheral" abbrev="Algorithm" name="ML_Algorithm"/>
            <FE fgColor="FFFFFF" bgColor="99004C" type="Peripheral" abbrev="Characteristic" name="Characteristic"/>
        </frame>
    </header>
    <definition>This frame represents ML models, identifiees ML Algorithm that produce the models, and model's Characteristics.</definition>
    <lexeme POS="N" name="model"/>
 
 
    <subCorpus name="01-unannotated-1">
        <sentence corpID="111" docID="421" sentNo="1" paragNo="181" aPos="0" ID="1215438">
            <text>This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields).</text>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="2" paragNo="181" aPos="0" ID="1215438">
            <text>Following Tesauros work on TD-Gammon, we used a 4,000 parameter feedforward neural network to develop a competitive backgammon evaluation function.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
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                    <label end="0" start="0" name="10.1023/A:1007417214905"/>
                    <label cBy="351" end="89" start="76" name="Target"/>
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                <layer rank="1" name="FE">
                <label end="89" start="68" name="ML_Algorithm"/>
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                <layer rank="1" name="GF"/>
                <layer rank="1" name="PT"/>
                <layer rank="1" name="Other"/>
                <layer rank="1" name="Sent"/>
                <layer rank="1" name="Verb"/>
            </annotationSet>
        </sentence>
        <sentence corpID="111" docID="421" sentNo="3" paragNo="181" aPos="0" ID="1215438">
            <text>Random forests (RFs) are collections of weak, weakly-correlated decision trees that function as ensembles of experts.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-013-5346-7"/>
                    <label cBy="351" end="76" start="64" name="Target"/>
                </layer>
                <layer rank="1" name="FE">
                <label end="19" start="0" name="Model"/>
                <label end="43" start="25" name="Model"/>
                <label end="115" start="64" name="Model"/>
                <label end="62" start="46" name="Characteristic"/>
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                <layer rank="1" name="GF"/>
                <layer rank="1" name="PT"/>
                <layer rank="1" name="Other"/>
                <layer rank="1" name="Sent"/>
                <layer rank="1" name="Verb"/>
            </annotationSet>
        </sentence>
        <sentence corpID="111" docID="421" sentNo="4" paragNo="181" aPos="0" ID="1215438">
            <text>ART, which stands for Association Rule Tree, builds decision lists that can be viewed as degenerate, polythetic decision trees.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
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                    <label end="0" start="0" name="10.1023/B:MACH.0000008085.22487.a6"/>
                    <label cBy="351" end="125" start="112" name="Target"/>
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                <layer rank="1" name="FE">
                <label end="2" start="0" name="ML_Algorithm"/>
                <label end="37" start="22" name="ML_Algorithm"/>
                <label end="65" start="52" name="Model"/>
                <label end="98" start="89" name="Characteristic"/>
                <label end="110" start="101" name="Characteristic"/>
            </layer>
                <layer rank="1" name="GF"/>
                <layer rank="1" name="PT"/>
                <layer rank="1" name="Other"/>
                <layer rank="1" name="Sent"/>
                <layer rank="1" name="Verb"/>
            </annotationSet>
        </sentence>
        <sentence corpID="111" docID="421" sentNo="5" paragNo="181" aPos="0" ID="1215438">
            <text>Decision tree based models (single decision trees, committees of trees, and random forests) consistently provided the best results.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-012-5322-7"/>
                    <label cBy="351" end="12" start="0" name="Target"/>
                    <label cBy="351" end="48" start="35" name="Target"/>
                </layer>
                <layer rank="1" name="FE">
                <label end="69" start="51" name="Model"/>
                <label end="48" start="27" name="Model"/>
                <label end="25" start="0" name="Model"/>
                <label end="89" start="76" name="Model"/>
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                <layer rank="1" name="Other"/>
                <layer rank="1" name="Sent"/>
                <layer rank="1" name="Verb"/>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="6" paragNo="181" aPos="0" ID="1215438">
            <text>These features are then typically employed in predictive models that can be constructed using, for example, SVMs or decision trees.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-010-5193-8"/>
                    <label cBy="351" end="61" start="56" name="Target"/>
                    <label cBy="351" end="129" start="116" name="Target"/>
                </layer>
                <layer rank="1" name="FE">
                <label end="61" start="46" name="Model"/>
                <label end="110" start="108" name="ML_Algorithm"/>
            </layer>
                <layer rank="1" name="GF"/>
                <layer rank="1" name="PT"/>
                <layer rank="1" name="Other"/>
                <layer rank="1" name="Sent"/>
                <layer rank="1" name="Verb"/>
            </annotationSet>
        </sentence>
        <sentence corpID="111" docID="421" sentNo="7" paragNo="181" aPos="0" ID="1215438">
            <text>Generative graphical models namely the Markov random fields (MRF) fit this purpose, which can represent complex spatial and temporal relations among sensors, producing interpretable answers in terms of probability.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
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                    <label end="0" start="0" name="10.1007/s10994-013-5399-7"/>
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                <layer rank="1" name="FE">
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                <label end="58" start="39" name="Model"/>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="8" paragNo="181" aPos="0" ID="1215438">
            <text>Given a dataset, traditional clustering algorithms often only provide a single set of clusters, a single view of the dataset.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-013-5350-y"/>
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                <layer rank="1" name="FE">
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                <layer rank="1" name="GF"/>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="9" paragNo="181" aPos="0" ID="1215438">
            <text>The core idea is based on the assumption that similar data points are very likely grouped together by some clustering algorithm and, conversely, data points that co-occur very often in the same cluster should be regarded as being very similar.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-013-5339-6"/>
                    <label cBy="351" end="64" start="54" name="Target"/>
                    <label cBy="351" end="155" start="145" name="Target"/>
                    <label cBy="351" end="200" start="194" name="Target"/>
                </layer>
                <layer rank="1" name="FE">
                <label end="64" start="54" name="Model"/>
                <label end="52" start="46" name="Characteristic"/>
                <label end="97" start="82" name="Characteristic"/>
                <label end="126" start="107" name="ML_Algorithm"/>
                <label end="155" start="145" name="Model"/>
                <label end="200" start="162" name="Characteristic"/>
                <label end="241" start="230" name="Characteristic"/>
 
            </layer>
                <layer rank="1" name="GF"/>
                <layer rank="1" name="PT"/>
                <layer rank="1" name="Other"/>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="10" paragNo="181" aPos="0" ID="1215438">
            <text>However, frequent patterns are not necessarily informative for the given learning problem.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-008-5089-z"/>
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        </sentence>
        <sentence corpID="111" docID="421" sentNo="11" paragNo="181" aPos="0" ID="1215438">
            <text>We review related work, then statistical-relational models, especially Parametrized Bayes nets and Markov Logic Networks.</text>
            <annotationSet cDate="05/02/2003 04:00:38 PDT Fri" status="MANUAL" ID="1877967">
                <layer rank="1" name="Target">
                    <label end="0" start="0" name="10.1007/s10994-012-5289-4"/>
                    <label cBy="351" end="57" start="52" name="Target"/>
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                <label end="57" start="29" name="Model"/>
                <label end="93" start="71" name="Model"/>
                <label end="119" start="99" name="Model"/>
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    </subCorpus>
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model_corpus.txt · Last modified: 2016/03/16 12:21 by pj