public static String[] stemWords(
Locale locale, Locale fallback, Collection<String> words, int minimalTermChars) {
SnowballProgram s = selectStemmer(locale, fallback);
if (s == null) {
return words.toArray(new String[words.size()]);
}
List<String> rl = new ArrayList<>(words.size());
for (final String w : words) {
s.setCurrent(w);
s.stem();
String c = s.getCurrent();
if (c.length() >= minimalTermChars) {
rl.add(c);
}
}
return rl.toArray(new String[rl.size()]);
}
/**
* Creates a Stem annotation with same begin and end as the AnnotationFS fs, the value is the
* stemmed value derived by applying the featurepath.
*
* @param jcas the JCas
* @param fs the AnnotationFS where the Stem annotation is created
* @throws AnalysisEngineProcessException if the {@code stem} method from the snowball stemmer
* cannot be invoked.
*/
private void createStemAnnotation(JCas jcas, AnnotationFS fs)
throws AnalysisEngineProcessException {
// Check for blank text, it makes no sense to add a stem then (and raised an exception)
String value = fp.getValue(fs);
if (!StringUtils.isBlank(value)) {
if (lowerCase) {
// Fixme - should use locale/language defined in CAS.
value = value.toLowerCase(Locale.US);
}
Stem stemAnnot = new Stem(jcas, fs.getBegin(), fs.getEnd());
SnowballProgram programm = getSnowballProgram(jcas);
programm.setCurrent(value);
try {
// The patched snowball from Lucene has this as a method on SnowballProgram
// but if we have some other snowball also in the classpath, Java might
// choose to use the other. So to be safe, we use a reflection here.
// -- REC, 2011-04-17
MethodUtils.invokeMethod(programm, "stem", null);
} catch (Exception e) {
throw new AnalysisEngineProcessException(e);
}
stemAnnot.setValue(programm.getCurrent());
stemAnnot.addToIndexes(jcas);
// Try setting the "stem" feature on Tokens.
Feature feat = fs.getType().getFeatureByBaseName("stem");
if (feat != null
&& feat.getRange() != null
&& jcas.getTypeSystem().subsumes(feat.getRange(), stemAnnot.getType())) {
fs.setFeatureValue(feat, stemAnnot);
}
}
}
/**
* This method creates the histogram of the document represented by this instance; if necessary
* the text is pre-processed (removing stopwords and stemming the remaining terms) before
* calculating the frequency of each one of its terms.
*
* @return The histogram of the terms of the document, represented by a map that has keys (the
* terms) of type {@link String}, and values (their relative frequencies) of type {@link
* Double}.
*/
private Map<String, Double> createHistogram() throws Exception {
DocumentClassifierApp application = DocumentClassifierApp.getApplication();
boolean isStemming = application.isStemming(false);
boolean isRemovalStopWords = application.isRemovalStopWords(false);
/**
* Even if the user has chosen (through the Preferences panel) not to apply stemming to document
* terms, it is necessary to create the classes that do it, otherwise the compiler will generate
* an error.
*/
Class stemClass = Class.forName(application.getStemmer(false));
SnowballProgram stemmer = (SnowballProgram) stemClass.newInstance();
@SuppressWarnings("unchecked")
Method stemMethod = stemClass.getMethod("stem", new Class[0]);
Object[] emptyArgs = new Object[0];
String specialCharacters = " \t\n\r\f,;.:!'\"()?[]=-@";
Map<String, Double> documentHistogram = new HashMap<String, Double>();
String currentToken;
Double frequency;
int weight;
String row;
/** I build the set of stopwords, by reading the appropriate file. */
Set<String> stopWordsList = new HashSet<String>();
BufferedReader stopWordsBR;
File stopWordsListFile =
new File(DocumentClassifierApp.getApplication().getStopWordsList(false));
String[] fields;
if (isRemovalStopWords) {
/**
* If the user has chosen not to enable the removal of stopwords, no element is added to the
* set, which remains empty.
*/
stopWordsBR = new BufferedReader(new FileReader(stopWordsListFile));
while ((row = stopWordsBR.readLine()) != null) {
if (!row.isEmpty()) {
fields = row.split("|");
if (!fields[0].startsWith(" ")) {
stopWordsList.add(fields[0].trim());
}
}
}
stopWordsBR.close();
}
/**
* Pre-processing of the text. The title and text of the document are represented as two
* strings, belonging to an array, such that I will be able to apply the same operations to both
* of them, but weighting the terms in a different way depending if they belong to the title or
* the text.
*/
String[] titleText = new String[2];
titleText[0] = title;
titleText[1] = text;
for (int j = 0; j <= 1; j++) {
if (j == 0) {
/**
* If I'm reading the title of the document-->Its terms have a double weight than the terms
* of the text, because they are more directly related to the argument and context of the
* document, than the terms of the text.
*/
weight = 2;
} else {
weight = 1;
}
titleText[j] = titleText[j].toLowerCase();
StringTokenizer ST = new StringTokenizer(titleText[j], specialCharacters);
while (ST.hasMoreTokens()) {
currentToken = ST.nextToken();
/**
* Removal of stopwords (if enabled by the user) and of numbers (in any case). The word
* 'removal' is inappropriate, because what is really done is simply to not take into
* consideration a term if it is present in the stopwords list, or if it represents a
* number. In these two cases the term is not added to the map which represents the
* histogram of the document.
*/
if (!stopWordsList.contains(currentToken) && !currentToken.matches("\\d+")) {
if (isStemming) {
/**
* Stemming of the current term: The stemmer creates a new term containing the root of
* the one given in input.
*/
stemmer.setCurrent(currentToken);
stemMethod.invoke(stemmer, emptyArgs);
currentToken = stemmer.getCurrent();
}
/**
* The frequency of the current term (eventually stemmed to its root) is read from the
* document's histogram, and updated (depending on the weight assigned to the current
* term).
*/
frequency = documentHistogram.get(currentToken);
if (frequency == null) {
frequency = 0.0;
}
documentHistogram.put(currentToken, frequency + weight);
}
}
}
/**
* The histogram has been completed-->Now it is necessary to normalize its frequencies to the
* length of the document, making them relative.
*/
double normalizationFactor = 0;
for (double I : documentHistogram.values()) {
// Calculation of the document's length after pre-processing
normalizationFactor += I;
}
// Normalization of frequencies (from absolute to relative)
for (String Token : documentHistogram.keySet()) {
documentHistogram.put(Token, documentHistogram.get(Token) / normalizationFactor);
}
/**
* I make sure that, once created, the histogram of this document can't be modified anymore
* (accidentally or intentionally) by other classes that read it.
*/
return Collections.unmodifiableMap(documentHistogram);
}
private void copy_from(englishStemmer other) {
B_Y_found = other.B_Y_found;
I_p2 = other.I_p2;
I_p1 = other.I_p1;
super.copy_from(other);
}
private void copy_from(DanishStemmer other) {
I_x = other.I_x;
I_p1 = other.I_p1;
S_ch = other.S_ch;
super.copy_from(other);
}