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The Adobe Experience Manager SDK
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 opennlp.tools.namefind;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import opennlp.tools.tokenize.WhitespaceTokenizer;
import opennlp.tools.util.Span;
/**
* Class for holding names for a single unit of text.
*/
public class NameSample implements Serializable {
private final String id;
private final List sentence;
private final List names;
private final String[][] additionalContext;
private final boolean isClearAdaptiveData;
/** The a default type value when there is no type in training data. */
public static final String DEFAULT_TYPE = "default";
public NameSample(String id, String[] sentence, Span[] names,
String[][] additionalContext, boolean clearAdaptiveData) {
this.id = id;
Objects.requireNonNull(sentence, "sentence must not be null");
if (names == null) {
names = new Span[0];
}
this.sentence = Collections.unmodifiableList(new ArrayList<>(Arrays.asList(sentence)));
List namesList = Arrays.asList(names);
Collections.sort(namesList);
this.names = Collections.unmodifiableList(namesList);
if (additionalContext != null) {
this.additionalContext = new String[additionalContext.length][];
for (int i = 0; i < additionalContext.length; i++) {
this.additionalContext[i] = new String[additionalContext[i].length];
System.arraycopy(additionalContext[i], 0, this.additionalContext[i], 0, additionalContext[i].length);
}
}
else {
this.additionalContext = null;
}
isClearAdaptiveData = clearAdaptiveData;
// Check that name spans are not overlapping, otherwise throw exception
if (this.names.size() > 1) {
for (int i = 1; i < this.names.size(); i++) {
if (this.names.get(i).getStart() < this.names.get(i - 1).getEnd()) {
throw new RuntimeException(String.format("name spans %s and %s are overlapped in file: %s",
this.names.get(i - 1), this.names.get(i), id));
}
}
}
}
/**
* Initializes the current instance.
*
* @param sentence training sentence
* @param names
* @param additionalContext
* @param clearAdaptiveData if true the adaptive data of the
* feature generators is cleared
*/
public NameSample(String[] sentence, Span[] names,
String[][] additionalContext, boolean clearAdaptiveData) {
this(null, sentence, names, additionalContext, clearAdaptiveData);
}
public NameSample(String[] sentence, Span[] names, boolean clearAdaptiveData) {
this(sentence, names, null, clearAdaptiveData);
}
public String getId() {
return id;
}
public String[] getSentence() {
return sentence.toArray(new String[sentence.size()]);
}
public Span[] getNames() {
return names.toArray(new Span[names.size()]);
}
public String[][] getAdditionalContext() {
return additionalContext;
}
public boolean isClearAdaptiveDataSet() {
return isClearAdaptiveData;
}
@Override
public int hashCode() {
return Objects.hash(Arrays.hashCode(getSentence()), Arrays.hashCode(getNames()),
Arrays.hashCode(getAdditionalContext()), isClearAdaptiveDataSet());
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (obj instanceof NameSample) {
NameSample a = (NameSample) obj;
return Arrays.equals(getSentence(), a.getSentence()) &&
Arrays.equals(getNames(), a.getNames()) &&
Arrays.equals(getAdditionalContext(), a.getAdditionalContext()) &&
isClearAdaptiveDataSet() == a.isClearAdaptiveDataSet();
}
return false;
}
@Override
public String toString() {
StringBuilder result = new StringBuilder();
// If adaptive data must be cleared insert an empty line
// before the sample sentence line
if (isClearAdaptiveDataSet())
result.append("\n");
for (int tokenIndex = 0; tokenIndex < sentence.size(); tokenIndex++) {
// token
for (Span name : names) {
if (name.getStart() == tokenIndex) {
// check if nameTypes is null, or if the nameType for this specific
// entity is empty. If it is, we leave the nameType blank.
if (name.getType() == null) {
result.append(NameSampleDataStream.START_TAG).append(' ');
}
else {
result.append(NameSampleDataStream.START_TAG_PREFIX).append(name.getType()).append("> ");
}
}
if (name.getEnd() == tokenIndex) {
result.append(NameSampleDataStream.END_TAG).append(' ');
}
}
result.append(sentence.get(tokenIndex)).append(' ');
}
if (sentence.size() > 1)
result.setLength(result.length() - 1);
for (Span name : names) {
if (name.getEnd() == sentence.size()) {
result.append(' ').append(NameSampleDataStream.END_TAG);
}
}
return result.toString();
}
private static String errorTokenWithContext(String[] sentence, int index) {
StringBuilder errorString = new StringBuilder();
// two token before
if (index > 1)
errorString.append(sentence[index - 2]).append(" ");
if (index > 0)
errorString.append(sentence[index - 1]).append(" ");
// token itself
errorString.append("###");
errorString.append(sentence[index]);
errorString.append("###").append(" ");
// two token after
if (index + 1 < sentence.length)
errorString.append(sentence[index + 1]).append(" ");
if (index + 2 < sentence.length)
errorString.append(sentence[index + 2]);
return errorString.toString();
}
private static final Pattern START_TAG_PATTERN = Pattern.compile("\\s]*))?>");
public static NameSample parse(String taggedTokens,
boolean isClearAdaptiveData) throws IOException {
return parse(taggedTokens, DEFAULT_TYPE, isClearAdaptiveData);
}
public static NameSample parse(String taggedTokens, String defaultType,
boolean isClearAdaptiveData) throws IOException {
// TODO: Should throw another exception, and then convert it into an IOException in the stream
String[] parts = WhitespaceTokenizer.INSTANCE.tokenize(taggedTokens);
List tokenList = new ArrayList<>(parts.length);
List nameList = new ArrayList<>();
String nameType = defaultType;
int startIndex = -1;
int wordIndex = 0;
// we check if at least one name has the a type. If no one has, we will
// leave the NameType property of NameSample null.
boolean catchingName = false;
for (int pi = 0; pi < parts.length; pi++) {
Matcher startMatcher = START_TAG_PATTERN.matcher(parts[pi]);
if (startMatcher.matches()) {
if (catchingName) {
throw new IOException("Found unexpected annotation" +
" while handling a name sequence: " + errorTokenWithContext(parts, pi));
}
catchingName = true;
startIndex = wordIndex;
String nameTypeFromSample = startMatcher.group(2);
if (nameTypeFromSample != null) {
if (nameTypeFromSample.length() == 0) {
throw new IOException("Missing a name type: " + errorTokenWithContext(parts, pi));
}
nameType = nameTypeFromSample;
}
}
else if (parts[pi].equals(NameSampleDataStream.END_TAG)) {
if (!catchingName) {
throw new IOException("Found unexpected annotation: " + errorTokenWithContext(parts, pi));
}
catchingName = false;
// create name
nameList.add(new Span(startIndex, wordIndex, nameType));
}
else {
tokenList.add(parts[pi]);
wordIndex++;
}
}
String[] sentence = tokenList.toArray(new String[tokenList.size()]);
Span[] names = nameList.toArray(new Span[nameList.size()]);
return new NameSample(sentence, names, isClearAdaptiveData);
}
}