What is a corpus?
A corpus is a collection of authentic text or audio organized into datasets. Authentic here means text written or audio spoken by a native of the language or dialect. A corpus can be made up of everything from newspapers, novels, recipes, radio broadcasts to television shows, movies, and tweets. In natural language processing, a corpus contains text and speech data that can be used to train AI and machine learning systems. If a user has a specific problem or objective they want to address, they’ll need a collection of data that supports, or at least is a representation of, what they’re looking to achieve with machine learning and NLP.
What are the features of a good corpus?
- Large corpus size:Generally, the larger the size of a corpus, the better. Large quantities of specialized datasets are vital to training algorithms designed to perform sentiment analysis.
- High-quality data:High quality is crucial when it comes to the data within a corpus. Due to the large volume of data required for a corpus, even minuscule errors in the training data can lead to large-scale errors in the machine learning system’s output.
- Clean data:Data cleansing is also vital for creating and maintaining a high-quality corpus. Data cleansing allows identifying and eliminating any errors or duplicate data to create a more reliable corpus for NLP.
- Balance:A high-quality corpus is a balanced corpus. While it can be tempting to fill a corpus with everything and anything available, if one doesn’t streamline and structure the data collection process, it could unbalance the relevance of the dataset.
What are the challenges regarding creating a corpus?
- Deciding the type of data needed to solve the problem statement
- Availability of data
- Quality of the data
- Adequacy of the data in terms of the amount