NLP models address noisy or unstructured data through preprocessing and robust model architectures. Preprocessing steps like text normalization, tokenization, and spell correction clean the data by removing irrelevant symbols, fixing typos, and standardizing formats. For instance, converting "Thx 4 ur help!!" to "Thanks for your help" makes the input more interpretable.
Models trained on diverse datasets that include noisy or informal text are better equipped to handle unstructured data. Subword tokenization, as used in BERT and GPT, helps process unknown words or misspellings by breaking them into smaller, recognizable units. Data augmentation techniques, such as introducing synthetic noise during training, improve robustness.
Despite these strategies, noisy data can still pose challenges, especially in low-resource languages or domains with highly variable inputs. Ensuring the availability of clean and representative training data is critical to overcoming these limitations. Libraries like spaCy and NLTK offer tools for preprocessing noisy text efficiently.