public class MachineReadingProperties extends Object
Constructor and Description |
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MachineReadingProperties() |
@Execution.Option(name="logger", gloss="Static logger for this entire class") public static Logger logger
@Execution.Option(name="datasetReaderClass", gloss="which GenericDataSetReader to use (needs to match the corpus in question)", required=true) public static Class<GenericDataSetReader> datasetReaderClass
@Execution.Option(name="datasetAuxReaderClass", gloss="which GenericDataSetReader to use for aux data set (needs to match the corpus in question)") public static Class<GenericDataSetReader> datasetAuxReaderClass
@Execution.Option(name="useNewHeadFinder", gloss="If false, use the original head (and worse) finding mechanism in GenericDataSetReader. This option is primarily around for legacy purposes.") public static boolean useNewHeadFinder
@Execution.Option(name="readerLogLevel", gloss="verbosity of the corpus reader") public static String readerLogLevel
@Execution.Option(name="serializeCorpora", gloss="if false, we do not attempt to serialize the train/test corpora after reading") public static boolean serializeCorpora
@Execution.Option(name="forceGenerationOfIndexSpans", gloss="if true (default), regenerate span annotations for trees") public static boolean forceGenerationOfIndexSpans
@Execution.Option(name="serializedEntityExtractorPath", gloss="where to store/load the serialized entity extraction model") protected static String serializedEntityExtractorPath
@Execution.Option(name="serializedEntityExtractionResults", gloss="where to store the serialized sentences containing the results of entity extraction") protected static String serializedEntityExtractionResults
@Execution.Option(name="entityGazetteerPath", gloss="location of entity gazetteer file (if you\'re using one) -- this is a temporary option") public static String entityGazetteerPath
@Execution.Option(name="entityClassifier", gloss="entity extractor class to use") public static Class<BasicEntityExtractor> entityClassifier
@Execution.Option(name="entityResultsPrinters", gloss="comma-separated list of ResultsPrinter subclasses to use for printing the results of entity extraction") public static String entityResultsPrinters
@Execution.Option(name="serializedRelationExtractorPath", gloss="where to store/load the serialized relation extraction model") protected static String serializedRelationExtractorPath
@Execution.Option(name="serializedRelationExtractionResults", gloss="where to store the serialized sentences containing the results of relation extraction") protected static String serializedRelationExtractionResults
@Execution.Option(name="relationFeatureFactoryClass", gloss="FeatureFactory class to use for generating features from relations for relation extraction") public static Class<? extends RelationFeatureFactory> relationFeatureFactoryClass
@Execution.Option(name="relationMentionFactoryClass", gloss="relation mention factory class to use.") public static Class<RelationMentionFactory> relationMentionFactoryClass
@Execution.Option(name="relationFeatures", gloss="comma-separated list of feature types to generate for relation extraction.") public static String relationFeatures
@Execution.Option(name="relationResultsPrinters", gloss="comma-separated list of ResultsPrinter subclasses to use for printing the results of relation extraction") public static String relationResultsPrinters
@Execution.Option(name="trainRelationsUsingPredictedEntities", gloss="if true, the relation extraction model trains using predicted rather than gold entity mentions") public static boolean trainRelationsUsingPredictedEntities
@Execution.Option(name="testRelationsUsingPredictedEntities", gloss="if true, the relation extraction model is evaluated using predicted rather than gold entity mentions.") public static boolean testRelationsUsingPredictedEntities
@Execution.Option(name="createUnrelatedRelations", gloss="If true, it creates automatically negative examples by generating all combinations between EntityMentions in a sentence") public static boolean createUnrelatedRelations
@Execution.Option(name="doNotLexicalizeFirstArg", gloss="If true, it does not create any lexicalized features from the first argument (needed for KBP)") public static boolean doNotLexicalizeFirstArg
@Execution.Option(name="useRelationExtractionModelMerging", gloss="If true, the relation extractor will use ExtractorMerger for annotation (not training)") public static boolean useRelationExtractionModelMerging
@Execution.Option(name="relationsToSkipDuringTraining", gloss="comma-separated list relation types to skip during training") public static String relationsToSkipDuringTraining
@Execution.Option(name="relationExtractionPostProcessorClass", gloss="additional (probably domain-dependent) annotator to postprocess relations") public static Class<Extractor> relationExtractionPostProcessorClass
@Execution.Option(name="relationClassifier", gloss="relation extractor class to use") public static Class<? extends BasicRelationExtractor> relationClassifier
@Execution.Option(name="serializedEventExtractorPath", gloss="where to store/load the serialized event extraction model") protected static String serializedEventExtractorPath
@Execution.Option(name="serializedEventExtractionResults", gloss="where to store the serialized sentences containing the results of event extraction") protected static String serializedEventExtractionResults
@Execution.Option(name="eventResultsPrinters", gloss="comma-separated list of ResultsPrinter subclasses to use for printing the results of event extraction") public static String eventResultsPrinters
@Execution.Option(name="trainEventsUsingPredictedEntities", gloss="if true, the event extraction model trains using predicted rather than gold entity mentions") public static boolean trainEventsUsingPredictedEntities
@Execution.Option(name="testEventsUsingPredictedEntities", gloss="if true, the event extraction model is evaluated using predicted rather than gold entity mentions") public static boolean testEventsUsingPredictedEntities
@Execution.Option(name="consistencyCheck", gloss="consistency checker class to use") public static Class<Extractor> consistencyCheck
@Execution.Option(name="trainPath", gloss=" path to the training file/directory") protected static String trainPath
@Execution.Option(name="auxDataPath", gloss="path to the aux training file/directory") protected static String auxDataPath
@Execution.Option(name="serializedTrainingSentencesPath", gloss=" where to store the serialized training sentences objects", required=true) protected static String serializedTrainingSentencesPath
@Execution.Option(name="serializedAuxTrainingSentencesPath", gloss="where to store the serialized aux training sentences objects") protected static String serializedAuxTrainingSentencesPath
@Execution.Option(name="loadModel", gloss="if true, load a serialized model rather than training a new one") protected static boolean loadModel
@Execution.Option(name="trainUsePipelineNER", gloss="during training, use NER generated by the CoreNLP pipeline") public static boolean trainUsePipelineNER
@Execution.Option(name="trainOnly", gloss="if true, don\'t run evaluation (implies forceRetraining)") protected static boolean trainOnly
@Execution.Option(name="testPath", gloss="path to the testing file/directory") protected static String testPath
@Execution.Option(name="serializedTestSentencesPath", gloss="where to store the serialized test sentence objects") protected static String serializedTestSentencesPath
@Execution.Option(name="extractEntities", gloss="whether to extract entities, or use gold-standard entities for relation/event extraction") protected static boolean extractEntities
@Execution.Option(name="extractRelations", gloss="whether we should extract relations") protected static boolean extractRelations
@Execution.Option(name="extractEvents", gloss="whether we should extract events") protected static boolean extractEvents
@Execution.Option(name="crossValidate", gloss="if true, run cross-validation") protected static boolean crossValidate
@Execution.Option(name="kfold", gloss="number of partitions in training data for cross validation") public static int kfold
@Execution.Option(name="percentageOfTrain", gloss="Pct of train partition to use for training (e.g. for RELMS experiment)") public static double percentageOfTrain
@Execution.Option(name="featureSimilarityThreshold") public static double featureSimilarityThreshold
@Execution.Option(name="computeFeatSimilarity") public static boolean computeFeatSimilarity
@Execution.Option(name="featureSelectionNumFeaturesRatio") public static double featureSelectionNumFeaturesRatio
@Execution.Option(name="L1Reg") public static boolean L1Reg
@Execution.Option(name="L2Reg") public static boolean L2Reg
@Execution.Option(name="L1RegLambda") public static double L1RegLambda