The correct answer is C. Word embedding.
Word embedding is a technique in natural language processing (NLP) that maps words or phrases to vectors of real numbers. This allows computers to represent the meaning of words in a way that can be used for tasks such as text classification, sentiment analysis, and machine translation.
Word embedding is a powerful technique that has been shown to be effective for a variety of NLP tasks. However, it is important to note that word embedding is not a perfect solution. For example, word embedding can be sensitive to the order of words in a sentence, and it can be difficult to interpret the meaning of the vectors that are produced.
Despite these limitations, word embedding is a valuable tool for NLP. It can be used to represent the meaning of words in a way that can be used for a variety of tasks.
Here are brief explanations of the other options:
- Data aggregation is the process of combining data from multiple sources into a single dataset. This can be done for a variety of purposes, such as to create a more complete picture of a situation or to identify trends.
- Data normalization is the process of converting data into a standard format. This can be done to make data easier to compare or to ensure that it is compatible with different software programs.
- Data imputation is the process of filling in missing values in a dataset. This can be done using a variety of methods, such as using the mean or median of the existing values.