NLP初学者寻求泰米尔语Tokenizer与Part of Speech Tagger代码支持

Hey there! As someone who’s worked with low-resource language NLP tools, I’ve got some solid recommendations for Tamil tokenizers and POS taggers across the languages you mentioned. Let’s break this down:

Python #

• NLTK with Tamil Support: NLTK includes a dedicated Tamil tokenizer module. After installing the necessary resources, you can use it like this:

  import nltk
  from nltk.tokenize.tamil import TamilTokenizer
  
  # Download Tamil resources if you haven't already
  nltk.download('tamil')
  
  tokenizer = TamilTokenizer()
  text = "தமிழ் மொழி உலகின் பழைய மொழிகளில் ஒன்று"
  tokens = tokenizer.tokenize(text)
  print(tokens)
  

• spaCy (Community Tamil Model): There’s a community-trained Tamil model for spaCy that includes a reliable tokenizer. First install the model, then use spaCy’s standard workflow:

  import spacy
  
  # Install the Tamil model via pip first
  nlp = spacy.load("ta_core_news_sm")
  doc = nlp("தமிழ் மொழி அழகு மிக்க மொழி")
  tokens = [token.text for token in doc]
  print(tokens)
  

Java #

• Apache OpenNLP: OpenNLP provides pre-trained Tamil tokenizer models. Here’s a quick implementation:

  import opennlp.tools.tokenize.TokenizerME;
  import opennlp.tools.tokenize.TokenizerModel;
  import java.io.FileInputStream;
  import java.io.InputStream;
  
  public class TamilTokenizerExample {
      public static void main(String[] args) throws Exception {
          // Replace with the path to your downloaded Tamil tokenizer model
          InputStream modelIn = new FileInputStream("ta-token.bin");
          TokenizerModel model = new TokenizerModel(modelIn);
          TokenizerME tokenizer = new TokenizerME(model);
          String text = "தமிழ் மொழி உலகின் பழைய மொழிகளில் ஒன்று";
          String[] tokens = tokenizer.tokenize(text);
          for (String token : tokens) {
              System.out.println(token);
          }
          modelIn.close();
      }
  }
  

  You’ll need to download the pre-trained Tamil tokenizer model from the OpenNLP model repository.

C #

• ICU4C: The ICU library has robust Unicode-aware tokenization support for Tamil. Use the BreakIterator class for word-level tokenization:

  #include <stdio.h>
  #include <unicode/brkiter.h>
  #include <unicode/utypes.h>
  #include <unicode/ustring.h>
  
  int main() {
      UErrorCode status = U_ZERO_ERROR;
      BreakIterator* bi = BreakIterator::createWordInstance(Locale::getTamil(), status);
      if (U_FAILURE(status)) {
          printf("Error creating BreakIterator\n");
          return 1;
      }
      const UChar text[] = L"தமிழ் மொழி உலகின் பழைய மொழிகளில் ஒன்று";
      bi->setText(text, u_strlen(text));
      int32_t start = bi->first();
      int32_t end = bi->next();
      while (end != BreakIterator::DONE) {
          UChar token[100];
          u_strncpy(token, text + start, end - start);
          token[end - start] = '\0';
          printf("%S\n", token);
          start = end;
          end = bi->next();
      }
      delete bi;
      return 0;
  }
  

  Compile this with links to the ICU4C libraries to get it working.

Python #

• NLTK Tamil POS Tagger: NLTK includes a tagged Tamil corpus that you can use to train a TnT tagger (or use pre-trained resources):

  import nltk
  from nltk.corpus import indian
  from nltk.tag import tnt
  
  # Download required resources
  nltk.download('indian')
  nltk.download('punkt')
  
  # Train the tagger on the Tamil POS corpus
  train_data = indian.tagged_sents('tamil.pos')
  tnt_tagger = tnt.TnT()
  tnt_tagger.train(train_data)
  
  # Tag your text
  text = "தமிழ் மொழி உலகின் பழைய மொழிகளில் ஒன்று"
  tokens = nltk.word_tokenize(text, language='tamil')
  tagged_tokens = tnt_tagger.tag(tokens)
  print(tagged_tokens)
  

• spaCy Tamil Model: The same spaCy Tamil model mentioned earlier also includes POS tagging out of the box:

  import spacy
  
  nlp = spacy.load("ta_core_news_sm")
  doc = nlp("தமிழ் மொழி அழகு மிக்க மொழி")
  for token in doc:
      print(f"{token.text} - {token.pos_}")
  

Java #

• Apache OpenNLP: OpenNLP has a pre-trained Tamil POS tagger model. Here’s how to implement it:

  import opennlp.tools.postag.POSModel;
  import opennlp.tools.postag.POSTaggerME;
  import java.io.FileInputStream;
  import java.io.InputStream;
  
  public class TamilPOSTaggerExample {
      public static void main(String[] args) throws Exception {
          // Replace with the path to your downloaded Tamil POS model
          InputStream modelIn = new FileInputStream("ta-pos-maxent.bin");
          POSModel model = new POSModel(modelIn);
          POSTaggerME tagger = new POSTaggerME(model);
          String[] tokens = {"தமிழ்", "மொழி", "உலகின்", "பழைய", "மொழிகளில்", "ஒன்று"};
          String[] tags = tagger.tag(tokens);
          for (int i = 0; i < tokens.length; i++) {
              System.out.println(tokens[i] + " - " + tags[i]);
          }
          modelIn.close();
      }
  }
  

  Grab the pre-trained Tamil POS model from the OpenNLP model repository to use this.

C #

• CRFsuite with Custom Training: ICU handles tokenization, but for POS tagging, your best bet is to use a CRF model trained on Tamil POS data. CRFsuite has C bindings that let you train and inference with a model. You can use the Tamil POS corpus from NLTK (convert it to CRFsuite’s input format) to train your tagger. Alternatively, for simpler use cases, you can build a rule-based tagger using hand-crafted patterns for common Tamil POS tags.

Hope these tools help you kickstart your Tamil NLP research! If you run into issues setting any of these up, feel free to follow up with more details.

内容的提问来源于stack exchange，提问作者S.EB