The Stanford CoreNLP Java library contains a lemmatizer that is a little resource intensive but I have run it on my laptop with <512MB of RAM.
To use it:
- Download the jar files;
- Create a new project in your editor of choice/make an ant script that includes all of the jar files contained in the archive you just downloaded;
- Create a new Java as shown below (based upon the snippet from Stanford’s site);
import java.util.Properties;
public class StanfordLemmatizer {
protected StanfordCoreNLP pipeline;
public StanfordLemmatizer() {
// Create StanfordCoreNLP object properties, with POS tagging
// (required for lemmatization), and lemmatization
Properties props;
props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma");
// StanfordCoreNLP loads a lot of models, so you probably
// only want to do this once per execution
this.pipeline = new StanfordCoreNLP(props);
}
public List<String> lemmatize(String documentText)
{
List<String> lemmas = new LinkedList<String>();
// create an empty Annotation just with the given text
Annotation document = new Annotation(documentText);
// run all Annotators on this text
this.pipeline.annotate(document);
// Iterate over all of the sentences found
List<CoreMap> sentences = document.get(SentencesAnnotation.class);
for(CoreMap sentence: sentences) {
// Iterate over all tokens in a sentence
for (CoreLabel token: sentence.get(TokensAnnotation.class)) {
// Retrieve and add the lemma for each word into the list of lemmas
lemmas.add(token.get(LemmaAnnotation.class));
}
}
return lemmas;
}
}