Noise in IR datasets refers to irrelevant or low-quality data that can negatively impact the retrieval process. To handle noise, IR systems typically use pre-processing techniques such as text cleaning (removing stopwords, special characters, and irrelevant content) and filtering out low-quality documents before indexing.
Another approach is using relevance feedback, where users provide input on whether the retrieved results are relevant, allowing the system to adjust and filter out noisy data over time.
Machine learning algorithms can also be applied to identify and remove noisy data by learning patterns of what constitutes relevant content and distinguishing it from irrelevant noise.