Hamming distance is a metric used to measure the number of positions at which two strings of equal length differ. It is primarily used in error detection and correction for binary data, where the distance between two binary strings indicates the number of bit flips needed to convert one string into the other.
For example, the Hamming distance between the binary strings "1101" and "1001" is 1, as they differ in only one position.
In NLP, Hamming distance can be applied to measure dissimilarities between sequences, such as spelling variations or minor edits in text strings. It is computationally efficient for fixed-length sequences and helps identify small changes or mutations in binary or textual data.
The Hamming distance is sensitive to the exact matching of characters, making it less suitable for semantic comparisons.