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Machine Learning in GATE: Classification and Annotation

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11 diapositives · 268 Views · Sep 22, 2014

This machine learning tool in GATE uses classification to classify annotations, with attributes to refer to instance annotations and different attribute types. It implements machine learning for pattern recognition in GATE, with training and application modes and the ability to save models and datasets. The standard use scenario involves preparing data, training, and applying the machine learning PR. This tool can be used to classify instances, export datasets, and save learned models. ML Engine Interface offers methods for adding training instances, classifying new instances, initializing the engine, setting dataset definition, setting options, and registering PR.

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Machine Learning in GATE: Classification and Annotation
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  1. Machine Learning in GATE Valentin Tablan

  2. Machine Learning in GATE • Uses classification. [Attr1, Attr2, Attr3, … Attrn]  Class • Classifies annotations. (Documents can be classified as well using a simple trick.) • Annotations of a particular type are selected as instances. • Attributes refer to instance annotations. • Attributes have a position relative to the instance annotation they refer to.

  3. Attributes Attributes can be: • Boolean The [lack of] presence of an annotation of a particular type [partially] overlapping the referred instance annotation. • Nominal The value of a particular feature of the referred instance annotation. The complete set of acceptable values must be specified a-priori. • Numeric The numeric value (converted from String) of a particular feature of the referred instance annotation.

  4. Implementation Machine Learning PR in GATE. Has two functioning modes: • training • application Uses an XML file for configuration: <?xml version="1.0" encoding="windows-1252"?> <ML-CONFIG> <DATASET> … </DATASET> <ENGINE>…</ENGINE> <ML-CONFIG>

  5. <DATASET> <DATASET> <INSTANCE-TYPE>Token</INSTANCE-TYPE> <ATTRIBUTE> <NAME>POS_category(0)</NAME> <TYPE>Token</TYPE> <FEATURE>category</FEATURE> <POSITION>0</POSITION> <VALUES> <VALUE>NN</VALUE> <VALUE>NNP</VALUE> <VALUE>NNPS</VALUE> … </VALUES> [<CLASS/>] </ATTRIBUTE> … </DATASET>

  6. <ENGINE> <ENGINE> <WRAPPER>gate.creole.ml.weka.Wrapper</WRAPPER> <OPTIONS> <CLASSIFIER>weka.classifiers.j48.J48</CLASSIFIER> <CLASSIFIER-OPTIONS>-K 3</CLASSIFIER-OPTIONS> <CONFIDENCE-THRESHOLD>0.85</CONFIDENCE-THRESHOLD> </OPTIONS> </ENGINE>

  7. Attributes Position Instances type: Token

  8. Machine Learning PR • Can save a learnt model to an external file for later use. Saves the actual model and the collected dataset. • Can export the collected dataset in .arff format.

  9. Standard Use Scenario Application • Prepare data by enriching the documents with annotation for attributes. (e.g. run Tokeniser, POS tagger, Gazetteer, etc). • [ Load the previously saved model. ] • Run the ML PR in application mode. • [ Save the learnt model. ] Training • Prepare training data by enriching the documents with annotation for attributes. (e.g. run Tokeniser, POS tagger, Gazetteer, etc). • Run the ML PR in training mode. • Export the dataset as .arff and perform experiments using the WEKA interface in order to find the best attribute set / algorithm / algorithm options. • Update the configuration file accordingly. • Run the ML PR again to collect the actual data. • [ Save the learnt model. ]

  10. An Example Learn POS category from POS context.

  11. Using Other ML Libraries The MLEngine Interface Method Summary • void addTrainingInstance(List attributes) Adds a new training instance to the dataset.  • Object classifyInstance(List attributes) Classifies a new instance.  • void init() This method will be called after an engine is created and has its dataset and options set.  • void setDatasetDefinition(DatasetDefintion definition) Sets the definition for the dataset used.  • void setOptions(org.jdom.Element options) Sets the options from an XML JDom element. • void setOwnerPR(ProcessingResource pr) Registers the PR using the engine with the engine.