/*
* Copyright 2012
* Ubiquitous Knowledge Processing (UKP) Lab
* Technische Universität Darmstadt
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package de.tudarmstadt.ukp.dkpro.core.opennlp;
import static org.apache.uima.fit.util.JCasUtil.indexCovered;
import static org.apache.uima.fit.util.JCasUtil.select;
import static org.apache.uima.fit.util.JCasUtil.toText;
import static org.apache.uima.util.Level.INFO;
import java.io.InputStream;
import java.nio.charset.Charset;
import java.nio.charset.StandardCharsets;
import java.util.Collection;
import java.util.Map;
import org.apache.uima.UimaContext;
import org.apache.uima.analysis_engine.AnalysisEngineProcessException;
import org.apache.uima.cas.CAS;
import org.apache.uima.cas.Type;
import org.apache.uima.fit.component.JCasAnnotator_ImplBase;
import org.apache.uima.fit.descriptor.ConfigurationParameter;
import org.apache.uima.fit.descriptor.TypeCapability;
import org.apache.uima.jcas.JCas;
import org.apache.uima.resource.ResourceInitializationException;
import de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.pos.POS;
import de.tudarmstadt.ukp.dkpro.core.api.parameter.ComponentParameters;
import de.tudarmstadt.ukp.dkpro.core.api.resources.CasConfigurableProviderBase;
import de.tudarmstadt.ukp.dkpro.core.api.resources.MappingProvider;
import de.tudarmstadt.ukp.dkpro.core.api.resources.MappingProviderFactory;
import de.tudarmstadt.ukp.dkpro.core.api.resources.ModelProviderBase;
import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence;
import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Token;
import de.tudarmstadt.ukp.dkpro.core.opennlp.internal.OpenNlpTagsetDescriptionProvider;
import opennlp.tools.postag.POSModel;
import opennlp.tools.postag.POSTaggerME;
/**
* Part-of-Speech annotator using OpenNLP.
*/
//NOTE: This file contains Asciidoc markers for partial inclusion of this file in the documentation
//Do not remove these tags!
// tag::capabilities[]
@TypeCapability(
inputs = {
"de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Token",
"de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.Sentence" },
outputs = {
"de.tudarmstadt.ukp.dkpro.core.api.lexmorph.type.pos.POS" })
public class OpenNlpPosTagger
extends JCasAnnotator_ImplBase
{
// end::capabilities[]
/**
* Use this language instead of the document language to resolve the model.
*/
public static final String PARAM_LANGUAGE = ComponentParameters.PARAM_LANGUAGE;
@ConfigurationParameter(name = PARAM_LANGUAGE, mandatory = false)
protected String language;
/**
* Override the default variant used to locate the model.
*/
public static final String PARAM_VARIANT = ComponentParameters.PARAM_VARIANT;
@ConfigurationParameter(name = PARAM_VARIANT, mandatory = false)
protected String variant;
/**
* Load the model from this location instead of locating the model automatically.
*/
public static final String PARAM_MODEL_LOCATION = ComponentParameters.PARAM_MODEL_LOCATION;
@ConfigurationParameter(name = PARAM_MODEL_LOCATION, mandatory = false)
protected String modelLocation;
/**
* The character encoding used by the model.
*/
public static final String PARAM_MODEL_ENCODING = ComponentParameters.PARAM_MODEL_ENCODING;
@ConfigurationParameter(name = PARAM_MODEL_ENCODING, mandatory = false)
private String modelEncoding;
/**
* Load the part-of-speech tag to UIMA type mapping from this location instead of locating
* the mapping automatically.
*/
public static final String PARAM_POS_MAPPING_LOCATION = ComponentParameters.PARAM_POS_MAPPING_LOCATION;
@ConfigurationParameter(name = PARAM_POS_MAPPING_LOCATION, mandatory = false)
protected String posMappingLocation;
/**
* Use the {@link String#intern()} method on tags. This is usually a good idea to avoid
* spaming the heap with thousands of strings representing only a few different tags.
*
* Default: {@code true}
*/
public static final String PARAM_INTERN_TAGS = ComponentParameters.PARAM_INTERN_TAGS;
@ConfigurationParameter(name = PARAM_INTERN_TAGS, mandatory = false, defaultValue = "true")
private boolean internTags;
/**
* Log the tag set(s) when a model is loaded.
*
* Default: {@code false}
*/
public static final String PARAM_PRINT_TAGSET = ComponentParameters.PARAM_PRINT_TAGSET;
@ConfigurationParameter(name = PARAM_PRINT_TAGSET, mandatory = true, defaultValue="false")
protected boolean printTagSet;
private CasConfigurableProviderBase<POSTaggerME> modelProvider;
private MappingProvider mappingProvider;
private Charset encoding;
@Override
public void initialize(UimaContext aContext)
throws ResourceInitializationException
{
super.initialize(aContext);
encoding = modelEncoding != null ? Charset.forName(modelEncoding) : null;
// tag::model-provider-decl[]
// Use ModelProviderBase convenience constructor to set up a model provider that
// auto-detects most of its settings and is configured to use default variants.
// Auto-detection inspects the configuration parameter fields (@ConfigurationParameter)
// of the analysis engine class and looks for default parameters such as PARAM_LANGUAGE,
// PARAM_VARIANT, and PARAM_MODEL_LOCATION.
modelProvider = new ModelProviderBase<POSTaggerME>(this, "tagger")
{
@Override
protected POSTaggerME produceResource(InputStream aStream)
throws Exception
{
// Load the POS tagger model from the location the model provider offers
POSModel model = new POSModel(aStream);
// end::model-provider-decl[]
// Extract tagset information from the model
OpenNlpTagsetDescriptionProvider tsdp = new OpenNlpTagsetDescriptionProvider(
getResourceMetaData().getProperty("pos.tagset"), POS.class,
model.getPosModel());
if (getResourceMetaData().containsKey("pos.tagset.tagSplitPattern")) {
tsdp.setTagSplitPattern(getResourceMetaData().getProperty(
"pos.tagset.tagSplitPattern"));
}
addTagset(tsdp);
if (printTagSet) {
getContext().getLogger().log(INFO, tsdp.toString());
}
// tag::model-provider-decl[]
// Create a new POS tagger instance from the loaded model
return new POSTaggerME(model);
}
};
// end::model-provider-decl[]
// tag::mapping-provider-decl[]
// General setup of the mapping provider in initialize()
mappingProvider = MappingProviderFactory.createPosMappingProvider(posMappingLocation,
language, modelProvider);
// end::mapping-provider-decl[]
}
@Override
public void process(JCas aJCas)
throws AnalysisEngineProcessException
{
// tag::model-provider-use-1[]
CAS cas = aJCas.getCas();
// Document-specific configuration of model and mapping provider in process()
modelProvider.configure(cas);
// end::model-provider-use-1[]
// tag::mapping-provider-use-1[]
// Mind the mapping provider must be configured after the model provider as it uses the
// model metadata
mappingProvider.configure(cas);
// end::mapping-provider-use-1[]
// When packaging a model, it is possible to store additional metadata. Here we fetch such a
// model metadata property that we use to determine if the tag produced by the tagger needs
// to be post-processed. This property is specific to the DKPro Core OpenNLP models
String tagSplitPattern = modelProvider.getResourceMetaData().getProperty(
"pos.tagset.tagSplitPattern");
Map<Sentence, Collection<Token>> index = indexCovered(aJCas, Sentence.class, Token.class);
for (Sentence sentence : select(aJCas, Sentence.class)) {
// tag::model-provider-use-2[]
Collection<Token> tokens = index.get(sentence);
String[] tokenTexts = toText(tokens).toArray(new String[tokens.size()]);
fixEncoding(tokenTexts);
// Fetch the OpenNLP pos tagger instance configured with the right model and use it to
// tag the text
String[] tags = modelProvider.getResource().tag(tokenTexts);
// end::model-provider-use-2[]
int i = 0;
for (Token t : tokens) {
String tag = tags[i];
// Post-process the tag if necessary
if (tagSplitPattern != null) {
tag = tag.split(tagSplitPattern)[0];
}
// tag::mapping-provider-use-2[]
// Convert the tag produced by the tagger to an UIMA type, create an annotation
// of this type, and add it to the document.
Type posTag = mappingProvider.getTagType(tag);
POS posAnno = (POS) cas.createAnnotation(posTag, t.getBegin(), t.getEnd());
// To save memory, we typically intern() tag strings
posAnno.setPosValue(internTags ? tag.intern() : tag);
posAnno.setCoarseValue(posAnno.getClass().equals(POS.class) ? null
: posAnno.getType().getShortName().intern());
posAnno.addToIndexes();
// end::mapping-provider-use-2[]
// Connect the POS annotation to the respective token annotation
t.setPos(posAnno);
i++;
}
}
}
private void fixEncoding(String[] aTokenTexts)
throws AnalysisEngineProcessException
{
// "Fix" encoding before passing to a model which was trained with encoding problems
if (encoding != null && !"UTF-8".equals(encoding.name())) {
for (int i = 0; i < aTokenTexts.length; i++) {
aTokenTexts[i] = new String(aTokenTexts[i].getBytes(StandardCharsets.UTF_8),
encoding);
}
}
}
}