Text data normalization

To process Czech and Slovak text for the purpose of extracting entities while also needing to normalize the text through stemming and lemmatization, you can follow a structured methodology that involves several steps. Here’s a recommended approach:

1. Text Preprocessing

  • Language Detection: If your dataset contains a mix of Czech and Slovak texts, start by detecting the language of each document to apply language-specific processing accurately.
  • Cleaning: Remove unnecessary elements such as HTML tags, URLs, emojis, and punctuation. For languages like Czech and Slovak, it's crucial to handle diacritics correctly since they can change the meaning of words.
  • Normalization: Convert your text to a uniform case (usually lowercase) to ensure consistency during processing.

2. Tokenization

Split the text into sentences and then into words (tokens). Given the complexity of Czech and Slovak grammar, use a tokenizer that understands the language structure well.

3. Part-of-Speech Tagging

Tag each word with its part of speech (noun, verb, adjective, etc.). This step is essential for effective lemmatization since the root form of a word often depends on its part of speech.

4. Lemmatization and Stemming

  • Lemmatization: Convert words to their base (dictionary) form. For Czech and Slovak, use a lemmatizer that understands the morphological rules of the language. Lemmatization is generally preferred over stemming for tasks requiring a high level of accuracy, such as entity extraction, because it preserves the semantic meaning of the word.
  • Stemming (optional): Reduce words to their stem or root form by cutting off the end of the word. Stemming is more aggressive than lemmatization and might not always produce linguistically correct outputs. However, it can be useful in reducing word variations and improving the performance of some text processing tasks.

5. Named Entity Recognition (NER)

Now that the text is preprocessed and normalized, use a Named Entity Recognition model trained on Czech and Slovak languages to identify and extract entities such as names of people, places, organizations, etc.

6. Post-processing

After extracting entities, you might want to further refine the results by removing duplicates, resolving entity references, or aggregating similar entities.

Tools and Libraries

  • For Preprocessing, Tokenization, and POS Tagging: Libraries such as NLTK, SpaCy, or Stanza offer tools for text preprocessing, tokenization, and part-of-speech tagging. Stanza, in particular, has support for many languages, including Czech and Slovak.
  • For Lemmatization: Use language-specific lemmatizers. The Czech National Corpus provides tools for Czech, and there are similar resources for Slovak.
  • For NER: Look for models specifically trained on Czech or Slovak languages. SpaCy and Stanza might offer pre-trained models for these languages. If not, you may need to train your model using annotated datasets in Czech or Slovak.

Best Practices

  • Evaluate Tools: Test different libraries and tools to see which ones perform best on your specific dataset.
  • Continuous Evaluation: Regularly evaluate the performance of your entity extraction pipeline and refine it based on feedback and new data.
  • Custom Training: Consider training your models on a domain-specific corpus if off-the-shelf models do not meet your accuracy needs.

This methodology combines linguistic processing with machine learning to achieve accurate entity extraction from Czech and Slovak texts. It's adaptable depending on the specific requirements of your project and the available resources.