Text Similarity Search via Normalized Compression Distance
This article discusses a technique called Normalized Compression Distance (NCD) for measuring text similarity, which can be used for efficient text similarity search.
Why it matters
NCD-based text similarity search is a simple yet powerful technique that can enable efficient information retrieval and text analysis applications.
Key Points
- 1NCD is a metric that measures the similarity between two texts by compressing them together and comparing the compressed size to the individual compressed sizes.
- 2NCD-based text similarity search can be more efficient than traditional methods like TF-IDF or word embeddings, especially for large text corpora.
- 3The article provides an example implementation of NCD-based text similarity search using the LZW compression algorithm.
Details
Normalized Compression Distance (NCD) is a technique for measuring the similarity between two texts by compressing them together and comparing the compressed size to the individual compressed sizes. This provides a way to quantify the information distance between the texts. NCD-based text similarity search can be more efficient than traditional methods like TF-IDF or word embeddings, especially for large text corpora, as it does not require building complex models. The article provides an example implementation of NCD-based text similarity search using the LZW compression algorithm, demonstrating how it can be used for efficient nearest-neighbor search on text data.
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