Handling missing data in NLP involves strategies to minimize its impact on model performance while preserving as much information as possible. The approach depends on the nature and extent of missing data.
- Imputation: Replace missing text with placeholders like
or the mean/most frequent term in the dataset. This is useful for models that can process unknown tokens. - Dropping Missing Rows: If the dataset is large and the missing data constitutes a small fraction, removing incomplete rows may be an efficient solution.
- Predictive Filling: Use models like GPT or BERT to generate plausible replacements based on the surrounding context, especially for missing words or phrases within sentences.
- Data Augmentation: Generate additional data samples to compensate for gaps. This approach is helpful when training data is scarce.
Pre-trained embeddings, such as Word2Vec or BERT, also mitigate the impact of missing data by assigning default or learned embeddings to unknown words. Ensuring robust handling of missing data is crucial for NLP tasks, especially in domains like customer support or medical records where incomplete inputs are common.