Of late, I've been busy converting a fairly large application from Lucene 2.4 to Lucene 2.9. There are quite a few improvements - see this white paper from Lucid Imagination for a quick overview (sign-up required). Along with the many performance improvements, there are some API changes and deprecations. Some of these deprecations came with a fair amount of warning, such as the Hits object which is finally going away in Lucene 3.0, but some others are scheduled more aggressively - there is a brand new API for TokenStream in Lucene 2.9, and the old API will also be removed in Lucene 3.0.
The new API is actually quite nice - its more flexible, so you can add custom properties to the Token object during analysis, simply by adding the required TokenFilter. A TokenFilter stores user-created Attributes for a Token, which can be retrieved during Analysis. Prior to this Attribute based API, you would probably use the Token's payload to do the same thing. In any case, the new API is described in detail in the Javadocs. I thought it would be interesting to actually build a TokenFilter using the docs, so I recycled some old ideas to create a Part of Speech tagging TokenFilter.
Custom Attribute - Interface and Implementation
The first step is to create a custom attribute to hold the information about the Part of Speech. The interface simply defines getters and setters for the Pos object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | // Source: src/main/java/net/sf/jtmt/postaggers/lucene/PosAttribute.java
package net.sf.jtmt.postaggers.lucene;
import net.sf.jtmt.postaggers.Pos;
import org.apache.lucene.util.Attribute;
/**
* Part of Speech Attribute.
*/
public interface PosAttribute extends Attribute {
public void setPos(Pos pos);
public Pos getPos();
}
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The implementation is the name of the interface suffixed by Impl, a convention enforced by the AttributeSource class. The class also extends AttributeImpl.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | // Source: src/main/java/net/sf/jtmt/postaggers/lucene/PosAttributeImpl.java
package net.sf.jtmt.postaggers.lucene;
import org.apache.lucene.util.AttributeImpl;
import net.sf.jtmt.postaggers.Pos;
public class PosAttributeImpl extends AttributeImpl implements PosAttribute {
private static final long serialVersionUID = -8416041956010464591L;
private Pos pos = Pos.OTHER;
public Pos getPos() {
return pos;
}
public void setPos(Pos pos) {
this.pos = pos;
}
@Override
public void clear() {
this.pos = Pos.OTHER;
}
@Override
public void copyTo(AttributeImpl target) {
((PosAttributeImpl) target).setPos(pos);
}
@Override
public boolean equals(Object other) {
if (other == this) {
return true;
}
if (other instanceof PosAttributeImpl) {
return pos == ((PosAttributeImpl) other).getPos();
}
return false;
}
@Override
public int hashCode() {
return pos.ordinal();
}
}
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TokenFilter
The Part of Speech TokenFilter is actually the meat of the class. The constructor takes in the necessary parameters to start it up, and the incrementToken() method implements the logic necessary to do the POS tagging. As I said before, I am just recycling old ideas here, so refer to the my old post if you have trouble following the code.
Essentially we open two TokenStreams one for the current token and one for the next token (since we cannot peek ahead, we need to have two streams). For each current token, we query Wordnet for the POS. If only a single POS is returned, then we just use that one. If no POS is returned, we use suffix rules to guess the POS. If multiple POS are returned, we use a set of inter-POS transition probabilities to determine the "most likely" POS for the current term.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | // Source: src/main/java/net/sf/jtmt/postaggers/lucene/PosTaggingFilter.java
package net.sf.jtmt.postaggers.lucene;
import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URL;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.TreeMap;
import net.sf.jtmt.clustering.ByValueComparator;
import net.sf.jtmt.postaggers.Pos;
import org.apache.commons.collections15.keyvalue.MultiKey;
import org.apache.commons.lang.StringUtils;
import org.apache.lucene.analysis.TokenFilter;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.tokenattributes.TermAttribute;
import edu.mit.jwi.Dictionary;
import edu.mit.jwi.IDictionary;
import edu.mit.jwi.item.IIndexWord;
/**
* Sets the POS tag for each token in the token stream. Uses Wordnet and some
* grammar rules to make an initial determination. If Wordnet returns multiple
* POS possibilities, we use the context surrounding the word (previous and
* next characters) and a table of pre-calculated probabilities (from the
* Brown corpus) to determine the most likely POS. If Wordnet returns a single
* POS, that is accepted. If Wordnet cannot determine the POS, then word
* suffix rules are used to guess the POS.
*/
public class PosTaggingFilter extends TokenFilter {
private String prevTerm = null;
private String nextTerm = null;
private Pos prevPos = null;
private TokenStream suffixStream;
private IDictionary wordnetDictionary;
private Map<String,Pos> suffixPosMap;
private Map<MultiKey<Pos>,Double> transitionProbabilities;
private PosAttribute posAttr;
private TermAttribute termAttr;
private TermAttribute suffixTermAttr;
protected PosTaggingFilter(TokenStream input,
TokenStream suffixStream,
String wordnetDictionaryPath, String suffixRulesPath,
String posTransitionProbabilitiesPath) throws Exception {
super(input);
// declare the POS attribute for both streams
this.posAttr = (PosAttribute) addAttribute(PosAttribute.class);
this.termAttr = (TermAttribute) addAttribute(TermAttribute.class);
this.suffixTermAttr =
(TermAttribute) suffixStream.addAttribute(TermAttribute.class);
this.prevTerm = null;
this.suffixStream = suffixStream;
this.input.reset();
this.suffixStream.reset();
// advance the pointer to the next token for the suffix stream
if (this.suffixStream.incrementToken()) {
this.nextTerm = suffixTermAttr.term();
}
// create artifacts for doing the POS tagging
this.wordnetDictionary = initWordnetDictionary(wordnetDictionaryPath);
this.suffixPosMap = initSuffixPosMap(suffixRulesPath);
this.transitionProbabilities = initTransitionProbabilities(
posTransitionProbabilitiesPath);
}
public final boolean incrementToken() throws IOException {
String currentTerm = null;
if (input.incrementToken()) {
currentTerm = termAttr.term();
} else {
return false;
}
if (suffixStream.incrementToken()) {
this.nextTerm = suffixTermAttr.term();
} else {
this.nextTerm = null;
}
termAttr.setTermBuffer(currentTerm);
// find the POS of the current word from Wordnet
List<Pos> currentPoss = getPosFromWordnet(currentTerm);
if (currentPoss.size() == 1) {
// unambiguous match, look no further
posAttr.setPos(currentPoss.get(0));
} else if (currentPoss.size() == 0) {
// wordnet could not find a POS, use suffix rules to find
if (prevTerm != null) {
// this is not thr first word, check for capitalization for Noun
if (currentTerm.charAt(0) == Character.UPPERCASE_LETTER) {
posAttr.setPos(Pos.NOUN);
}
}
if (posAttr.getPos() != null) {
Pos pos = getPosFromSuffixRules(currentTerm);
posAttr.setPos(pos);
}
} else {
// wordnet reported multiple POS, find the best one
Pos pos = getMostProbablePos(currentPoss, nextTerm, prevPos);
posAttr.setPos(pos);
}
this.prevTerm = currentTerm;
this.prevPos = posAttr.getPos();
return true;
}
private IDictionary initWordnetDictionary(String wordnetDictionaryPath)
throws Exception {
IDictionary wordnetDictionary = new Dictionary(
new URL("file", null, wordnetDictionaryPath));
wordnetDictionary.open();
return wordnetDictionary;
}
private Map<String, Pos> initSuffixPosMap(String suffixRulesPath)
throws Exception {
Map<String,Pos> suffixPosMap = new TreeMap<String, Pos>(
new Comparator<String>() {
public int compare(String s1, String s2) {
int l1 = s1 == null ? 0 : s1.length();
int l2 = s2 == null ? 0 : s2.length();
if (l1 == l2) {
return 0;
} else {
return (l2 > l1 ? 1 : -1);
}
}
});
BufferedReader reader = new BufferedReader(new FileReader(suffixRulesPath));
String line = null;
while ((line = reader.readLine()) != null) {
if (StringUtils.isEmpty(line) || line.startsWith("#")) {
continue;
}
String[] suffixPosPair = StringUtils.split(line, "\t");
suffixPosMap.put(suffixPosPair[0], Pos.valueOf(suffixPosPair[1]));
}
reader.close();
return suffixPosMap;
}
private Map<MultiKey<Pos>,Double> initTransitionProbabilities(
String transitionProbabilitiesPath) throws Exception {
Map<MultiKey<Pos>,Double> transitionProbabilities =
new HashMap<MultiKey<Pos>,Double>();
BufferedReader reader = new BufferedReader(
new FileReader(transitionProbabilitiesPath));
String line = null;
int row = 0;
while ((line = reader.readLine()) != null) {
if (StringUtils.isEmpty(line) || line.startsWith("#")) {
continue;
}
String[] cols = StringUtils.split(line, "\t");
for (int col = 0; col < cols.length; col++) {
MultiKey<Pos> key = new MultiKey<Pos>(Pos.values()[row], Pos.values()[col]);
transitionProbabilities.put(key, Double.valueOf(cols[col]));
}
row++;
}
reader.close();
return transitionProbabilities;
}
private List<Pos> getPosFromWordnet(String currentTerm) {
List<Pos> poss = new ArrayList<Pos>();
for (Pos pos : Pos.values()) {
try {
IIndexWord indexWord = wordnetDictionary.getIndexWord(
currentTerm, Pos.toWordnetPos(pos));
if (indexWord != null) {
poss.add(pos);
}
} catch (NullPointerException e) {
// JWI throws NPE if it cannot find a word in dictionary
continue;
}
}
return poss;
}
private Pos getPosFromSuffixRules(String currentTerm) {
for (String suffix : suffixPosMap.keySet()) {
if (StringUtils.lowerCase(currentTerm).endsWith(suffix)) {
return suffixPosMap.get(suffix);
}
}
return Pos.OTHER;
}
private Pos getMostProbablePos(List<Pos> currentPoss, String nextTerm,
Pos prevPos) {
Map<Pos,Double> posProbs = new HashMap<Pos,Double>();
// find the possible POS values for the previous and current term
if (prevPos != null) {
for (Pos currentPos : currentPoss) {
MultiKey<Pos> key = new MultiKey<Pos>(prevPos, currentPos);
double prob = transitionProbabilities.get(key);
if (posProbs.containsKey(currentPos)) {
posProbs.put(currentPos, posProbs.get(currentPos) + prob);
} else {
posProbs.put(currentPos, prob);
}
}
}
// find the possible POS values for the current and previous term
if (nextTerm != null) {
List<Pos> nextPoss = getPosFromWordnet(nextTerm);
if (nextPoss.size() == 0) {
nextPoss.add(Pos.OTHER);
}
for (Pos currentPos : currentPoss) {
for (Pos nextPos : nextPoss) {
MultiKey<Pos> key = new MultiKey<Pos>(currentPos, nextPos);
double prob = transitionProbabilities.get(key);
if (posProbs.containsKey(currentPos)) {
posProbs.put(currentPos, posProbs.get(currentPos) + prob);
} else {
posProbs.put(currentPos, prob);
}
}
}
}
// now find the current Pos with the maximum probability
if (posProbs.size() == 0) {
return Pos.OTHER;
} else {
ByValueComparator<Pos,Double> bvc =
new ByValueComparator<Pos,Double>(posProbs);
List<Pos> posList = new ArrayList<Pos>();
posList.addAll(posProbs.keySet());
Collections.sort(posList, Collections.reverseOrder(bvc));
return posList.get(0);
}
}
}
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Analyzer
For the Analyzer, I used a StandardAnalyzer and tacked on the POS TokenFilter to it. So as it spits out the Token with the term in it, it will also contain the PosAttribute, which the user can access if desired. The code is written as a JUnit test, since I was really testing the TokenFilter.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | // Source: src/test/java/net/sf/jtmt/postaggers/lucene/PosTaggingFilterTest.java
package net.sf.jtmt.postaggers.lucene;
import java.io.Reader;
import java.io.StringReader;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.lang.StringUtils;
import org.apache.lucene.analysis.Analyzer;
import org.apache.lucene.analysis.TokenStream;
import org.apache.lucene.analysis.standard.StandardTokenizer;
import org.apache.lucene.analysis.tokenattributes.TermAttribute;
import org.apache.lucene.util.Version;
import org.junit.Test;
/**
* Test for the Lucene POS Tagging Filter.
*/
public class PosTaggingFilterTest {
private String[] INPUT_TEXTS = {
"The growing popularity of Linux in Asia, Europe, and the US is " +
"a major concern for Microsoft.",
"Jaguar will sell its new XJ-6 model in the US for a small fortune.",
"The union is in a sad state.",
"Please do not state the obvious.",
"I am looking forward to the state of the union address.",
"I have a bad cold today.",
"The cold war was over long ago."
};
private Analyzer analyzer = new Analyzer() {
@Override
public TokenStream tokenStream(String fieldName, Reader reader) {
return new StandardTokenizer(Version.LUCENE_CURRENT, reader);
}
};
@Test
public void testPosTagging() throws Exception {
for (String inputText : INPUT_TEXTS) {
System.out.println("Input: " + inputText);
List<String> tags = new ArrayList<String>();
TokenStream input = analyzer.tokenStream(
"f", new StringReader(inputText));
TokenStream suffixStream =
analyzer.tokenStream("f", new StringReader(inputText));
input = new PosTaggingFilter(input, suffixStream,
"/opt/wordnet-3.0/dict", "src/main/resources/pos_suffixes.txt",
"src/main/resources/pos_trans_prob.txt");
TermAttribute termAttribute =
(TermAttribute) input.addAttribute(TermAttribute.class);
PosAttribute posAttribute =
(PosAttribute) input.addAttribute(PosAttribute.class);
while (input.incrementToken()) {
tags.add(termAttribute.term() + "/" + posAttribute.getPos());
}
input.end();
input.close();
StringBuilder tagBuf = new StringBuilder();
tagBuf.append("Tagged: ").append(StringUtils.join(tags.iterator(), " "));
System.out.println(tagBuf.toString());
}
}
}
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Results
The test inputs are identical to the test in the old post, although the new code seems to have difficulty figuring out verbs. Here are the results (cleaned up a bit for display).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | Input: The growing popularity of Linux in Asia, Europe, and the US is
a major concern for Microsoft.
Tagged: The/OTHER growing/ADJECTIVE popularity/NOUN of/OTHER Linux/NOUN
in/ADJECTIVE Asia/NOUN Europe/NOUN and/OTHER the/OTHER US/NOUN
is/OTHER a/NOUN major/ADJECTIVE concern/NOUN for/OTHER
Microsoft/OTHER
Input: Jaguar will sell its new XJ-6 model in the US for a small fortune.
Tagged: Jaguar/NOUN will/NOUN sell/NOUN its/OTHER new/ADVERB XJ-6/OTHER
model/ADJECTIVE in/NOUN the/OTHER US/NOUN for/OTHER a/NOUN
small/ADJECTIVE fortune/NOUN
Input: The union is in a sad state.
Tagged: The/OTHER union/NOUN is/OTHER in/ADJECTIVE a/NOUN sad/ADJECTIVE
state/NOUN
Input: Please do not state the obvious.
Tagged: Please/VERB do/VERB not/ADVERB state/NOUN the/OTHER obvious/ADJECTIVE
Input: I am looking forward to the state of the union address.
Tagged: I/ADJECTIVE am/NOUN looking/ADJECTIVE forward/NOUN to/OTHER the/OTHER
state/NOUN of/OTHER the/OTHER union/ADJECTIVE address/NOUN
Input: I have a bad cold today.
Tagged: I/ADJECTIVE have/NOUN a/NOUN bad/ADJECTIVE cold/NOUN today/NOUN
Input: The cold war was over long ago.
Tagged: The/OTHER cold/ADJECTIVE war/NOUN was/OTHER over/ADVERB long/VERB
ago/ADJECTIVE
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Although the new TokenStream API is on a short deprecation fuse, its probably not such a big deal since most people don't use it directly. From a migration point of view, even if you do use the TokenStream API directly, its probably going to be less code than say, search or indexing code, so I guess its all good.
