SimHash — The Ultimate Guide And How To Get Started Guide In Python

by | Jan 2, 2023 | Natural Language Processing

What is SimHash?

Simhash is a technique for generating a fixed-length “fingerprint” or “hash” of a variable-length input, such as a document or a piece of text. It is similar to a hash function and a form of local sensitive hashing but is designed to be more resistant to collision attacks, in which two different inputs produce the same hash. Simhash works by dividing the input into smaller chunks, called “features,” and then generating a hash of each feature. These hashes are then combined to produce the final hash for the input.

Simhash is often used for document deduplication, spam detection, and near-duplicate detection. It is essential to identify similar or duplicate content even when it has been slightly modified or rearranged. It works well for these kinds of tasks because it can find similarities between inputs even if they differ in a small number of ways. The main advantage is that it can be computed quicker and more efficiently than other text similarity algorithms.

What is near duplicate detection?

Near duplicate detection is a process used to identify and determine the similarity between pieces of text or documents. It involves comparing two or more texts and assessing their degree of similarity. The goal is to identify documents that are almost identical or nearly duplicate in content, even if they may have slight variations, such as rephrased sentences, synonyms, or different formatting.

Near duplicate detection is commonly employed in various applications, including plagiarism detection, document clustering, information retrieval, and content management systems. Identifying near-duplicates helps to ensure the integrity of content, avoid redundancy, improve search results, and assist in identifying potential instances of plagiarism or copyright infringement.

The process of near duplicate detection typically involves using algorithms and techniques such as tokenization, text similarity measures (e.g., cosine similarity, Jaccard similarity), and hashing methods. These methods enable the comparison of textual representations and the calculation of similarity scores, allowing for the identification of near duplicates.

Overall, near duplicate detection plays a crucial role in managing and analyzing large volumes of text-based data, helping to organize, classify, and maintain the quality of information.

How does SimHash work?

Simhash works by dividing the input into smaller chunks, called “features,” and then generating a hash of each feature. These hashes are then combined to produce the final simhash for the input.

Here is a more detailed description of the process:

  1. Split the input into a set of features. This can be done using various techniques, such as tokenizing the input into words or n-grams or extracting specific pieces of information, such as dates or names.
  2. Generate a hash for each feature. This is typically done using a standard hash function, such as SHA-1 or MD5.
  3. Combine the feature hashes to produce the final hash. This is typically done by concatenating the hashes and generating a new hash of the concatenated value. Alternatively, the feature hashes can be combined using bitwise operations, such as XOR.

To compare two simhashes, we can calculate the “distance” between them by counting the number of different bits in the two hashes. The smaller the distance, the more similar the two inputs are.

Simhash is designed to be more resistant to collision attacks than traditional hash functions. This is because it considers multiple input features rather than just the input as a whole. It makes it more difficult for an attacker to produce two inputs with the same hash, even if they are slightly different.

Example of SimHash Calculation

Simhash (similarity hash) is a hashing algorithm commonly used for near duplicate detection. It captures semantic content and generates a fingerprint or hash value for a given document or text. Here’s an example to illustrate how simhash works:

Let’s consider two sentences:

Sentence 1: “The quick brown fox jumps over the lazy dog.”
Sentence 2: “The lazy dog is jumped over by a quick brown fox.”

Step 1: Tokenization

Both sentences are tokenized into individual words, ignoring common stop words:

Sentence 1 tokens: [quick, brown, fox, jumps, lazy, dog]
Sentence 2 tokens: [lazy, dog, jumped, quick, brown, fox]

Step 2: Feature vector representation

Each token is converted into a feature vector, typically using a technique like the term frequency-inverse document frequency (TF-IDF). For simplicity, let’s assume the TF-IDF values are as follows:

Sentence 1 vector: [0.5, 0.4, 0.3, 0.7, 0.2, 0.9]
Sentence 2 vector: [0.2, 0.9, 0.6, 0.5, 0.4, 0.3]

Step 3: Calculating simhash

Simhash generates a binary fingerprint by comparing the weighted feature vectors. If the value is positive for each feature, the corresponding bit in the fingerprint is set to 1; otherwise, it is set to 0. Let’s calculate the simhash for both sentences:

Initialize an array of 0s, representing the fingerprint:

Fingerprint: [0, 0, 0, 0, 0, 0]

Compare the feature vectors element by element:

  • For the first feature, 0.5 in Sentence 1 and 0.2 in Sentence 2, the bit is set to 1 in the fingerprint.
    Fingerprint: [1, 0, 0, 0, 0, 0]
  • For the second feature, 0.4 in Sentence 1 and 0.9 in Sentence 2, the bit is set to 0.
    Fingerprint: [1, 0, 0, 0, 0, 0]
  • Continue comparing and updating the fingerprint for all features.

After comparing all features, the resulting fingerprint for the given sentences might look like this:
Fingerprint: [1, 0, 1, 0, 0, 0]

The simhash value is the binary fingerprint, which can be used to compare the similarity of documents. Similar documents are expected to have a higher number of common bits in their simhash values.

Please note that the example above is a simplified representation of simhash and actual implementations involve additional steps and optimizations to handle large-scale text data efficiently.

Use cases of SimHash

Simhash is often used for document deduplication, spam detection, and near-duplicate detection. It is essential to identify similar or duplicate content even when it has been slightly modified or rearranged.

Simhash is good at detecting duplicate content.

Simhash is good at detecting duplicate content at scale.

Some specific use cases include:

  • Deduplicating a large document dataset: it can identify and eliminate duplicate documents in a dataset. This can be very helpful when crawling the web, where it is common to find multiple copies of the same page.
  • Detecting spam emails: it can identify spam emails with variations on the same basic message, even if the specific words or phrases used in the letter have been changed.
  • Finding documents that are almost the same: it can be used to find records that are similar but not the same, like papers that have been copied and pasted or articles that have been slightly changed from their original versions.
  • Bots and automated accounts can be found on social media platforms by finding accounts that post similar or identical content.

Simhash is particularly well-suited to these tasks because it can detect similarities between inputs even when they differ by a small number of features. In addition, it is relatively fast and efficient to compute.

Advantages

Using Simhash to do things like find duplicate documents, find spam, and find near-duplicates has many benefits:

  1. Resistant to collision attacks: Simhash is designed to be more resistant to collision attacks than traditional hash functions because it considers multiple features of the input rather than just the input as a whole. This makes it more difficult for an attacker to produce two inputs with the same simhash, even if they are slightly different.
  2. It can detect similarities between inputs: Simhash can see similarities even when they differ by a small number of features. This makes it particularly useful for tasks such as spam detection, where it is vital to identify variations on the same basic message.
  3. Fast and efficient to compute: Simhash is relatively quick and efficient to calculate, making it suitable for large datasets.
  4. Easy to implement: The Simhash algorithm is relatively simple, making it easy to implement in various programming languages.

Overall, it is a helpful tool for tasks requiring identifying similar or duplicate content, even when it has been slightly modified or rearranged.

Disadvantages

Using Simhash for tasks like document deduplication, spam detection, and near-duplicate detection could have a few problems:

  1. It may not be as effective at detecting subtle differences between inputs: While Simhash can notice similarities between inputs even when they differ by a few features, it may be less effective at detecting subtle differences between inputs. This means that two inputs that are very similar but not identical may be treated as being completely different by the Simhash algorithm.
  2. Sensitive to the choice of features: The effectiveness can be heavily influenced by the specific features used to generate the hash. Choosing the wrong parts can lead to poor performance, while choosing the right components can significantly improve the algorithm’s accuracy.
  3. Not a replacement for traditional hash functions: Simhash is explicitly designed for document deduplication, spam detection, and near-duplicate detection. It is not a replacement for conventional hash functions, which are generally more suitable for data integrity checking and storage tasks.

While there are several advantages, it is essential to carefully consider whether it is the right tool for the specific task and carefully choose the features that will be used to generate the hash.

SimHash vs MinHash

Simhash and Minhash are both techniques for generating a fixed-length “fingerprint” or “hash” of a variable-length input, such as a document or a piece of text. Both algorithms are used for tasks such as document deduplication, spam detection, and near-duplicate detection, where it is essential to identify similar or duplicate content even when it has been slightly modified or rearranged.

One key difference between Simhash and Minhash is how they generate the input hash. Simhash divides the input into smaller chunks, called “features,” and then generates a hash of each feature. These hashes are then combined to produce the final hash for the input. In contrast, Minhash generates a hash of each possible feature of the information rather than just the features present in the input. This makes Minhash more efficient and faster to compute than Simhash, particularly for large datasets.

Another difference between Simhash and Minhash is how they compare two inputs to determine their similarity. Simhash determines the distance between two simhashes, calculated by counting the number of different bits in the two hashes. Minhash, on the other hand, compares the overlap between the sets of features present in the two inputs. This allows Minhash to detect subtle differences between inputs more accurately than Simhash.

Overall, both Sim/Min-hash are helpful tools for removing duplicates from documents, finding spam, and finding near-duplicates. However, they have different trade-offs and may be better or worse for other tasks depending on the needs.

How to use SimHash in Python

Here is an example of how to use hashlib in Python for similarity detection:

import hashlib

def simhash(input):
  # Split the input into a set of features
  features = extract_features(input)
  
  # Generate a hash for each feature
  hashes = [hashlib.sha1(feature).hexdigest() for feature in features]
  
  # Combine the feature hashes to produce the final simhash
  concatenated_hash = ''.join(hashes)
  simhash = hashlib.sha1(concatenated_hash).hexdigest()
  
  return simhash

def compare_simhashes(simhash1, simhash2):
  # Convert simhashes to integers
  int_simhash1 = int(simhash1, 16)
  int_simhash2 = int(simhash2, 16)
  
  # Calculate the distance between the simhashes
  distance = bin(int_simhash1 ^ int_simhash2).count('1')
  
  return distance

# Calculate the simhash for two pieces of text
text1 = "The quick brown fox jumps over the lazy dog."
text2 = "The quick brown fox jumps over the lazy cat."
simhash1 = simhash(text1)
simhash2 = simhash(text2)

# Compare the simhashes
distance = compare_simhashes(simhash1, simhash2)
print(f"Distance between simhashes: {distance}")

# Determine how similar the texts are based on the simhash distance
if distance < 5:
  print("Texts are very similar.")
elif distance < 10:
  print("Texts are somewhat similar")
else:
  print("Texts are not similar")

In this example, the distance between the simhashes is calculated by performing a bitwise XOR on the two simhashes and then counting the number of “1” bits in the result. The distance is then used to determine how similar the two pieces of text are. If the distance is small, the texts are considered very similar, while a larger distance indicates that the texts are not as similar.

Of course, this is one way to compare simhashes and determine similarities. The specific details of the comparison process will depend on the application’s specific requirements.

Conclusion

In conclusion, simhash generates a fixed-length “fingerprint” or “hash” of a variable-length input, such as a document or a text. It is particularly useful for tasks such as document deduplication, spam detection, and near-duplicate detection, where it is essential to identify similar or duplicate content even when it has been slightly modified or rearranged. Simhash works by dividing the input into smaller chunks, called “features,” and then generating a hash of each feature. These hashes are then combined to produce the final hash for the input.

Simhash is designed to be more resistant to collision attacks than traditional hash functions. It can detect similarities between inputs even when they differ by a small number of features. However, it may not be as effective at detecting subtle differences between inputs and is sensitive to the choice of features. Overall, Simhash is a valuable tool for tasks that require identifying similar or duplicate content, even when that content has been slightly modified or rearranged.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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